A theory of consolidation for reasoning about devices

Int. J. Man-Machine Studies (1991) 35, 467-489

A theory of consolidation for reasoning about devices ToM BYLANDER

Laboratory for Artificial bltelligence Research, Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210, USA (Received 16August 1989and accepted b7 revised form 29April 1990) Given a collection of components connected in a certain way, how can the behavioral descriptions of the components be composed into a behavioral description of the collection as a whole? We propose a theory of consolidation based on a conceptual representation of behavior. The behaviors of components are represented using a small number of primitive types of behaviors. The behavior of a device is inferred using rules of composition that describe how one type of behavior can arise from a structural combination of other types of behaviors.

1. Introduction Naive physics is the subject of how the physical world can be understood by naive intelligent agents, who are naive because they are not students of physics, but are intelligent because they can still reason efficiently and effectively about the physical world. Because people are prime examples of such agents, and because computers have the potential for powerful reasoning, research on naive physics attempts to answer the questions: How do people reason about physical phenomena? How can computers be endowed with similar facilities? Artificial intelligence research on naive physics concentrates on the second question, and by doing so, also seeks to achieve significant insight on the first (Weld & de Kleer, 1989). We are interested in a computational understanding of the information processing task of consolidationt----of composing the behavioral description of a collection of interconnected components (a "device") from the behavioral descriptions of the components (Bylander & Chandrasekaran, 1985). The idea is to infer the behavior of the device via rules that combine behavioral descriptions. For example, consider a device consisting of a light bulb, a switch, and a battery connected in an electrical circuit. Inferring that light is produced when the switch is closed might proceed by consolidation as follows. Each component of this device allows electricity to flow through it (the switch has a condition that it must be closed). Because of the way the components are connected, these "allow behaviors" combine to allow electricity to flow through a circuit when the switch is closed. Combined with the voltage produced by the battery, movement of electricity around the circuit when the switch is closed can be inferred. Combined with the light bulb's behavior to produce light when electricity flows through it, consolidation should infer that light is produced when the switch is closed. Note that this line of inference directly constructs a general behavioral description of the light bulb device from those of the components. 1"Unfortunately, "consolidation" is used in psychology to refer to the process of storing information into long-term memory. No relationship to this use of "consolidation" is intended. 467 0020-7373/91/100467 + 23503.00/0

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Consolidation differs from qualitative simulation and envisioning (de Kleer & Brown, 1984; Forbus, 1984; Kuipers, 1986), the most common methods for inferring behavior, in that they use rules of simulation rather than rules 0.t~ combination. Qualitative simulation and envisioning would infer the light-producing behavior of the light bulb device by explicitly considering states of the device (i.e. state variables are assigned qualitative values consistent with the behavioral descriptions of the components and connections) and simulating what states would occur when the switch becomes closed and stays closed. In addition, qualitative simulation requires a scenario to be input (i.e. initial state and external interactions); thus, its answer is specific to that scenario rather than to the device's behavior in general (Bylander, 1988). Envisioning, which generates all possible qualitative states and state transitions, is clearly not computationally appealing. This does not imply that consolidation can substitute for these methods. If specific states and state transitions must be predicted, then consolidation is inappropriate to use.t On the other hand, if behavioral generalizations are required without resort to exhaustive simulation, then consolidation is appropriate. We propose a theory of consolidation based on a conceptual representation of behavior. The behaviors of components are represented using a small number of primitive types of behaviors. The behavior of a device is inferred using rules of composition (called causal patterns) that describe how one type of behavior can arise from a structural combination of other types of behaviors. An implementation of our theory demonstrates its realizability. Our theory of consolidation can be considered phenomenological, in the general sense of that term. We describe how inferences about device behavior can be made via rules if combination, but we do not explain the conditions that give rise to these inferences. In particular, we do not provide a logical, psychological, or physics justification of our theory. Instead, we hope to persuade the reader that consolidation is a plausible naive physics task and is given a computationally plausible account by our theory. In the naive physics framework of Hayes (1985b), our theory of consolidation corresponds to a "cluster"---a set of closely related concepts, which, when taken together, form a theory of some aspect of naive physics. The types of behavior and causal patterns in our theory are intended for form a cluster for the task of consolidation. The integration of this cluster with other clusters (qualitative simulation, temporal reasoning, spatial reasoning, and so on (Weld & de Kleer, 1989)) is a problem for future research. Below, we explain our theory of consolidation, presenting the basic representations and inferences of our theory and discussing augmentations that increase its capability. The example of the light bulb device is used to illustrate our theory. Other examples are briefly described. Finally, unresolved issues are discussed.

2. Theory of consolidation Our theory of consolidation provides a conceptual basis for inferring the behavior of a device. The intuition underlying our theory is that components interact with other components via "stuff" or "substances" that move between components. What does t Consolidation followed by qualitative simulation might be appropriate, however (Dormoy, 1988).

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a component have so that interactions involving substances can occur? We believe that a commonsense answer has two parts, cf. Bobrow (1984). A component has structural relationships to substances and other components. A component can have connections to other components; these connections are intermediate points in the movement of substances between components. Also, a component can have contahzers of substances; containers are endpoints of substance movement (or intermediate points if the movement involves more than two components). A component has behavioral relationships to substances, how it acts and is acted upon by substances. This can range from a passive path for a substance (e.g. a pipe) to active involvement in the movement of a substance (e.g. a pump). Within naive physics research in AI, reasoning about device structure and component behavior is not the only proposal for inferring device behavior. In particular, Rieger and Grinberg (1977) and others (Patil et al., 1982; Pople, 1982; Weiss et al., 1978) propose a representation of physical situations that is based on describing the causal interactions (causal links) between the events that can occur in a physical situation. The limitation of causal link representations is in reasoning about changes to structure and behavior. The causal links assume that the device will maintain its structure, and that the components will maintain their behavior. As a result, any change invalidates the causal links, and reasoning about representations of structure and behavior is needed to infer the device's behavior (Bylander, 1990). 2.1. REPRESENTING STRUCTURE AND B E H A V I O R

This section discusses the elements of our theory for representing the structure of devices and behavior of components. The following section describes how device behavior can be inferred. 2.1.1. Structural primitives Up to this point, we have been implicitly assuming that the elements of a device can be separated into two classes: Components form the topology of the device. Their interaction causes the device's behavior. Wires, switches, pumps, and tanks are examples of components. Even empty spaces must sometimes be treated as components. If an empty space is an important part of a device's topology, e.g. the device depends on the empty space to be a path for a substance, then there is no choice but to represent the empty space as part of the topology, i.e. as a component. Substances are the means by which components interact with other components. Substances are any physical phenomena that, in some sense, move or flow from one place to another. Fluid, heat, and electricity are examples of substances. As shown by these examples, our use of "subst~mce" is not limited to flow of material. "Heat is a substance" does not imply that heat is naively represented as material, but only that heat is kind of physical phenomena that moves. If heat flow is the interaction between two components (e.g. within a heat exchanger), then heat must be considered a substance.

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The need for substances to move between components and for components to hold substances suggests two types of structural relationships: Connection between components. Like de Kleer and Brown (1984), we use connection to signify that one component is attached to another component or is otherwise in meaningful spatial contact with it. An example of "meaningful spatial contact" is the relationship of the surface of a light bulb with the space around it, which in turn, might be in contact with something that interacts with light, e.g. a photoelectric cell or a prism. We assume that each component has a fixed number of available connections to other components. Containment of substances. Contahzers represent the places inside components that substances can move from, move into, move through, and be at rest. A pipe, for example, can contain water. Substances might also be contained within other substances, e.g. water can contain heat and dissolved material. We assume that each container holds a single kind of substance. A container in our terminology does not imply significant capacity, so the phrase "X has a container for Y" only implies that there is some place inside X where Y can be located or moving, not that X has a significant capacity for Y (consider wires and electricity). The importance of the notion of containment for naive physics theories has been pointed out by Hayes (1985a) and Forbus (1984). For example, the light bulb device in Figure 1 has three components (denoted by boxes in the figure): a light bulb, a switch, and a battery.t Three substances are involved in the operation of this device: electricity, light, and "messages".1: The light bulb has three connections: two electricity connections called " e n d l " and "end2", and a light connection called "surface". Inside of the light bulb, there are places where electricity passes through and where light is produced. To model this, containers (denoted by circles) called "electrical" and "light source" are attributed to the light bulb. Like the light bulb, the switch and battery have connections and containers corresponding to the substances with which they interact. Note that there are two open connections in Figure 1: the "gate" connection of the switch and the

Light Bulb

Battery

|

Switch

- - - -

Isensorl

T.~ FIGURE 1. Structure of a light bulb device. i" To simplify this and further discussion, the wires connecting these components have been omitted. :[: Messages are not physical phenomena, but an abstraction of the physical level. We use messages as a convenience when the actual substance that carries the content is not important to the analysis.

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"light" connection of the light bulb. Open connections represent the places where the device interacts with the environment. Each connection, open connection, and container is treated as a location-or place in the device. A "path" is a network of two or more places with two endpoints. A circuit can be described by using the same place for both endpoints. In Figure 1, there appears to be a circular path of electrical connections and containers through which electricity can move. At this point, however, no commitment is made about whether electricity can move or will move around the circuit. For example, when the switch is open, no electricity can flow through the switch. On the other hand, the structure does limit what paths are possible. Light, in this model, cannot move into or out of the switch since the switch has no connection or container for light.

2.1.2. Types of behaviors When a component of a device contains a substance, the component has the opportunity to act upon the substance, e.g. by restricting its movement, by pushing or pulling it, or by transforming it to another kind of substance. Similarly, the substance might affect the component, changing how the component interacts with other substances. The central feature of the representation of behavior in our theory of consolidation is a small set of primitive schemas, called types of behaviors, which are used to describe the interactions between components and substances and to support reasoning about combinations of components. None of the types of behaviors are subsumed by other types of behaviors. If several types of behaviors apply to a component, then the behavioral description of the component should specify several behaviors. The types of behaviors are the following:

Allow.t The component permits a specified kind of substance to move from one place to another. For example, a wire has an allow electricity behavior between its connections. A pipe has an allow fluid behavior between its connections. Allow behaviors come in two subtypes: (1) movement is permitted in either direction, such as within a wire or pipe, and (2) movement is permitted only in one direction, such as within a diode or heart valve. These are respectively called two-way and one-way allow behaviors. Expel The component tries to move a substance out of an internal container, e.g. a capacitor has an expel electricity behavior, a balloon has an expel air behavior. Pump. The component tries to move a specified kind of substance through a path, e.g. a battery has a pump electricity behavior from its negative terminal to its positive terminal, a heart chamber has a pump blood behavior. The difference between pump and expel behaviors is that a pump operates on a path and expel operates on a single location. Move. Neither the expel nor the pump type of behavior makes any commitment about whether some movement is occurring; that is accounted for by the move type of behavior. A move behavior specifies that the component moves a specified kind of substance from one container to another (or around a circuit) along a specified path. For example, electrical circuits often have move electricity t When the term "allow" is used to refer to the allow type of behavior, it is italicized. The names of the other types of behaviors are treated similarly.

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behaviors. A heat exchanger has a move heat behavior. Move behaviors are implicitly constrained by the amount and capacity of the containers. Create. The component creates a specified kind of substance in a container, e.g. a light bulb has a create light behavior. A stereo speaker has a create sound behavior. Destroy. The component destroys a specified kind of substance in a container, e.g. an opaque material has a destroy light behavior, an acoustic insulator has a destroy sound behavior. A transformation of one substance into another can be represented by a combination of create and destroy behaviors. Change mode. When an electrical switch is closed, it has an allow electricity behavior; when the switch is open, it does not have an allow electricity behavior. We describe the switch as having two behavioral modes, operating regions that are associated with different sets of behaviors. An additional type of behavior, called change mode, specifies a predicate on behavior and the next behavioral mode of the component. For example, an electrical switch has a change mode behavior from open to closed when it receives an " o n " message. In research on natural language understanding, both Schank (1975) and Wilks (1973) propose "primitive semantic units" (Wilk's phrase) for representing physical actions and physical objects. Many of their primitives have meanings similar to our types of behavior. For example the P R O P E L action primitive proposed by Schank, which means "apply a force to", is similar to the pump type of behavior; the CONT primitive proposed by Wilks, which means "being a container", is similar to our containment structural primitive~ Neither Schank nor Wilks, however, propose how their primitives can be used to infer device behavior.

2.1.3. Description of the light bulb device With this repertoire of behaviors, behavioral descriptions of the components of the light bulb device presented in Figure 1 can now be given. Figure 2 displays the behavioral description of the switch. The description is split into four sections, with the names of sections in boldface, keywords in italics, and labels in typewriter font. The first two sections describe the structure of the switch. The connections are named endl, end2, and gate. The containers are named electrical and sensor. The names are intended to facilitate the reader's understanding of the description, but interpreting the representation does not depend on what names are selected. Each connection and container is specific to a single substance. The next section lists the behavioral modes of the switch, named open and closed. The modes section is included in a behavioral description only if there is more than one behavioral mode. The final section lists the behaviors of the switch. When it is in the closed m o d e , the switch has an allow electricity behavior from one end to the other through its electrical container. The phrase "between... a n d . . . " signifies a two-way allow behavior. The switch also has an allow message behavior from its message connection to its message container. "from... t o . . . " signifies a one-way allow behavior. The switch does not store the messages that are sent to it, so it must have a destroy message behavior. There are two change mode behaviors. The switch changes its behavioral mode from open to closed when it receives an on message. It

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Connections: endl of electricity end2 of electricity gate of message Containers: electrical of electricity sensor of message Modes: open closed Behaviors: allow electricity between endl and end2 thru electrical, mode closed allow message from gate to sensor destroy message in sensor change mode to closed when [move message from gate to sensor, content on], mode open change mode to open when [move message from gate to sensor, content off], mode closed FIGURE2. Behavioraldescription of the switch. changes mode from closed to open when it receives an off message, content is a parameter associated with messages. Note that there is no section specifically for substances; they are specified in the declarations of connections, containers, and behaviors. The behavioral description of the battery is given in Figure 3. The battery has two electricity connections, negative terminal and positive terminal, and one electricity container, electrical. It has two behaviors. Its pump electricity behavior goes through the whole battery, and its source is the electrical container. The battery's allow behavior also goes through the battery and is a two-way allow. The light bulb (Figure 4) has three connections and two containers. It has an allow electricity behavior between its two electricity connections and through its one electricity container, and an allow light behavior between its light connection and container. The light bulb also has create light, expel light, and destroy light behaviors, all of which are located in the light source container. The destroy light behavior is needed when the light that is created cannot, for whatever reason, move out of the light bulb. Since the light bulb is unable to store light, the light Connections: negative terminal of electricity positive terminal of electricity Containers: electrical of electricity Behaviors: pump electricity from negative terminal to positive terminal thru electrical, source electrical allow electricity between negative terminal and positive terminal thru electrical FIGURE3. Behavioraldescription of the battery.

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T. BYLANDER Connections: endl of electricity end2 of electricity surface of light Containers: electrical of electricity light source of light Behaviors: allow electricity between endl and end2 thru electrical allow light between light source and surface create light in light source expel light from light source destroy light in light source FIGURE4. Behavioraldescription of the light bulb.

"disappears". The light is actually transformed into heat, but to simplify the example, the light bulb's heat behaviors are omitted. Finally, Figure 5 describes the structure of the light bulb device. As in de Kleer and Brown (1984), a "component library" contains behavior descriptions of general types of components, each of which can be multiply-instantiated for a specific device. The names of the component types and the components in the device are chosen for the benefit of the reader, and not the representation. Non-electrical connections in the components' descriptions, such as the surface of the light bulb, happen to be open connections, so they are not mentioned in the description. 2.2. INFERRING BEHAVIOR The previous section describes how the behavior of components and the structure of devices are represented. This section describes how behavior is inferred from this information.

2. 2.1. Causal patterns Composing behaviors is based on the observation that the behaviors of components are defined over paths and paths can be easily composed. We further assume that the same types of behaviors used to predicate paths within components can also be used to predicate combinations of paths. The problem then becomes one of enumerating the patterns in which different behaviors over structurally related paths imply (cause) a behavior over the combination of the paths. One of our primary results is the identification of several such causal patterns. A causal pattern states that if certain types of behaviors satisfy a specific structural Components: battery instance of Battery switch instance of Switch light bulb instance of Light Bulb Connections: positive terminal of battery and end1 of switch end2 of switch and endl of light bulb end2 of light bulb and negative terminal of battery FIGURE5. Structural description of the light bulb device.

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r e l a t i o n s h i p , t h e n a n o t h e r b e h a v i o r o f a specified t y p e m i g h t b e c a u s e d . t F o r e x a m p l e , t h e propagate pump c a u s a l p a t t e r n specifies t h a t a pump b e h a v i o r in a serial r e l a t i o n s h i p with an allow b e h a v i o r c a u s e s a n o t h e r pump b e h a v i o r , e . g . a pump electricity b e h a v i o r b e t w e e n A a n d B, a n d an allow e l e c t r i c i t y b e h a v i o r b e t w e e n B a n d C c a u s e s a pump b e h a v i o r b e t w e e n A a n d C. T h e following a r e t h e c a u s a l p a t t e r n s t h a t w e h a v e i d e n t i f i e d . B e s i d e s a n E n g l i s h d e s c r i p t i o n o f e a c h c a u s a l p a t t e r n , w e also give a logical a p p r o x i m a t i o n . I n t h e f o r m u l a s , e a c h si is a v a r i a b l e f o r s u b s t a n c e s , p i for p a t h s , a n d l~ f o r l o c a t i o n s ( c o n n e c t i o n s o r c o n t a i n e r s ) . A l l v a r i a b l e s in an a n t e c e d e n t a r e u n i v e r s a l l y quantified.

Serial[parallel allow. An allow b e h a v i o r is c a u s e d b y t w o s e r i a l o r p a r a l l e l allow behaviors.~:

allow(s, Pl) ^ allow(s, P2) A s e r i a l ( p 1 , P2) ~ allow(s, P3) w h e r e P3 is t h e s e r i a l c o m b i n a t i o n o f p~ a n d P2

allow(s, Pl) ^ allow(s, P2) ^ p a r a l l e l ( p l , P2) ::)' allow(s, P3) w h e r e P3 is t h e p a r a l l e l c o m b i n a t i o n o f p~ a n d P2 F o r e x a m p l e , t w o p i p e s with b o t h e n d s c o n n e c t e d satisfy t h e parallel allow p a t t e r n , as well as t h e serial allow p a t t e r n ( t h e p i p e s f o r m a circuit). A n o t h e r c o n d i t i o n o f this p a t t e r n is t h a t t h e t w o allow b e h a v i o r s m u s t p e r m i t m o v e m e n t in t h e s a m e d i r e c t i o n . T h u s o n e c o m p o n e n t t h a t p e r m i t s m o v e m e n t f r o m A to B a n d a n o t h e r c o m p o n e n t t h a t p e r m i t s m o v e m e n t f r o m C to B, b u t n o t f r o m B t o C, w o u l d n o t result a n allow b e h a v i o r f r o m A to C. Propagate expel. A pump b e h a v i o r is c a u s e d b y an allow b e h a v i o r a n d a n expel b e h a v i o r t h a t is l o c a t e d at an e n d p o i n t o f t h e allow.

allow(s, p) ^ expel(s, l) ^ e n d p o i n t ( l , p) ~ pump(s, p) F o r e x a m p l e , t h e expel air b e h a v i o r o f a b a l l o o n c o m b i n e s with a n allow air b e h a v i o r f r o m t h e b a l l o o n to give rise to a pump air b e h a v i o r o v e r t h e s a m e p a t h as t h e allow. The s o u r c e o f t h e pump b e h a v i o r is t h e " a i r c o n t a i n e r " o f t h e balloon. Include expel. A pump b e h a v i o r is c a u s e d b y a pump b e h a v i o r a n d an expel b e t i a v i o r t h a t is l o c a t e d at an e n d p o i n t o f t h e pump.

pump(s, p) ^ expel(s, 1) ^ e n d p o i n t ( l , p) ~ pump(s, p) t Whether this behavior actually occurs depends on the physics of the substance and the details of the subbehaviors. For example, a pump behavior in one direction can cancel another pump behavior in the opposite direction. How this information can be taken into account is described in a later section. For the moment, we shall assume that the causal patterns are categorical inferences. ~:"Serial" and "parallel" have technical meanings in this paper. Two behaviors are serial if they have an endpoint in common, but do not otherwise intersect. For example, a behavior from A to B is serial to a behavior from B to C if the two paths only have B in common. It the two behaviors have both endpoints in common, then their serial combination forms a circuit. For example, a behavior from A to B is serial to a behavior from B to A if the two paths only have A and B in common. A behavior around a circuit cannot be serial to other behaviors. Two behaviors are parallel if they have the same endpoints, and neither behavior completely contains the other, i.e. a behavior cannot be in parallel with itself or with any of its sub-behaviors. For example, one behavior from A to B is parallel to another behavior from A to B if neither path contains the other. Also, a behavior around a circuit cannot be parallel to other behaviors.

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For example, a pump air behavior into a tire (such as using an air pump to pressurize a tire) is opposed by the tire's expel air behavior. Thus, the total pump behavior is a combination of the air pump's and tire's influences. The sources of the inferred pump behavior are the sources of the pump sub-behavior (the pump behavior used to make the inference) and the location of the expel sub-behavior. Propagate pump. A pump behavior is caused by a primp and an allow behavior in serial.

pump(s, Pl) ^ allow(s, P2) ^ serial(pl, P2) ~ pump(s, P3) where P3 is the serial combination of Pl and P2 For example, the pump electricity behavior of a battery and the allow electricity behavior of a wire connected to the battery results in a pump electricity behavior over the wire and battery. Serial/parallel pump. A pump behavior is caused by two pump behaviors in serial or parallel.

pump(s, Pl) n pump(s, P2) ^ serial(p1, P2) ~ pump(s, P3) where P3 is the serial combination of pl and P2

pump(s, Pl) n pump(s, P2) ^ parallel(p1, P2) =)' pump(s, P3) where P3 is the parallel combination of p~ and P2 The pump electricity behaviors of two batteries in serial give rise to a pump electricity behavior over both batteries. The sources of the inferred behaviors is a combination of the sources of the subbehaviors. Pump move. A move behavior is caused by a pump behavior a n d an allow behavior, both on the same path from a source of the substance to a sink of the substance, or both on the same circuit.

pump(s, p) ^ allow(s, p) ~ move(s, p) Two containers of water connected by a horizontal pipe (which causes the allow behavior) result in movement if there is a pump behavior between the containers. A wire connecting both ends of a battery is an example of the pump move causal pattern over a circuit. Additional conditions are that the beginning of the path must be a source of the substance and the end of the path must be a sink of the substance. Serial/parallel move. A move behavior is caused by two serial or parallel move behaviors.

move(s, Pl) ^ move(s, Pz) ^ serial(p1, P2) :z~move(s, P3) where P3 is the serial combination ofp~ and P2

move(s, Pl) ^ move(s, P2) ^ parallel(p1, P2) ::~ move(s, P3) where P3 is the serial combination of pl and P2

The pump move causal pattern provides for the inference of "primitive" move behaviors, while the serial move and parallel move patterns compose move behaviors.

Carry move. This causal pattern pertains to situations in which one substance contains another substance. A move behavior of a substance s~ that contains a

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substance s2 (e.g. water can contain heat) causes a move s2 behavior along the same path.

move(st, p) ^ contains(st, s2)::> move(s2, p) For example, when something that contains heat moves from A to B, heat also moves from A to B. We do not claim that this list is complete. Additional patterns might be required to reason about concepts like momentum, in which movement leads to additional influences, and about forces like gravity, in which one object causes influences at a distance. The absence of any causal patterns that imply expel, create, or destroy behaviors is also notable. This is because no structural pattern of behaviors will lead to the inference of any of these types of behaviors at a new location; the capability to expel, create, or destroy must have been inherently there in the first place. The causal patterns are similar to individual views and process descriptions in qualitative process theory (Forbus, 1984). They all are for expressing the conditions that give rise to behavior. The main difference is that the causal patterns are intended to be generic inferences for all substances. Although individual views and process descriptions can be stated at a high level of generality, there is no commitment in qualitative process theory to any particular level of generality, e.g. in practice, different types of substances such as liquid, gas, and heat have different process descriptions. Within qualitative process theory, the causal patterns might be expressed as "universal" individual views and process descriptions. The work of Sussman and Steele (1980) shares the goal of reasoning about a group of components as a single abstract component, which is embodied in their notion of "slices". A slice is a special kind of constraint that expresses part of the combined behavior of a group of components. By applying slices it is possible to decompose a device in different ways and to derive sets of constraints that are easier to reason about. However, their proposal is unsuitable for performing consolidation because slices are conditioned on how components of various types are connected rather than how behaviors are connected. Thus, a slice is not a general rule about behavior, but about a particular configuration of components.

2.2.2. blferring the behaviors of the battery-switch Suppose that the battery and the switch of the light bulb device (Figure 1) are consolidated. The behaviors of the battery-switch are inferred as follows: Using the serial allow pattern, an allow electricity behavior between the negative terminal of the battery and end2 of the switch is inferred from the allow electricity behavior of the battery between its terminals and the allow electricity of the switch between its two electricity connections.

allow electricity, (negative terminal, electrical, positive terminal)battery) ^ allow(electricity, (end1, electrical, end2)switch) ^ serial(battery path, switch path) allow(electricity, (negative terminalb~ttc~ . . . . . end2switch)) Using the propagate pump pattern, a pump behavior from the negative terminal of the battery to end2 of the switch is inferred from the pump electricity behavior

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of the battery between its terminals and the allow electricity of the switch between its two electricity connections.

pump(electricity, (negative terminal, electrical, positive terminal)battery) ^ allow(electricity, (endl, electrical, end2)switch) A serial(battery path, switch path) ~pump(electricity, (negative terminalt, attcry. . . . . end2switch)) 2.2.3. Inferring behavioral modes The behaviors inferred for the battery-switch need to take the behavioral mode of the switch's allow behavior into account. Since the battery's behaviors always happen (more precisely, the model asserts that they always happen) and the switch's allow behavior occurs only during the switch's closed mode, the behavioral mode of each inferred behavior is also the closed mode. In general, the behavioral mode of an inferred behavior is the intersection of the behavioral modes of the subbehaviors. However, one kind of interaction between causal patterns and behavioral modes affects the inference of behaviors and behavioral modes. Consider the situation in Figure 6, in which a light bulb and a switch are parallel to each other. The parallel allow causal pattern would imply an allow electricity behavior between A and B through the switch and light bulb during the closed mode of the switch. However, during the open mode, there is still an allow electricity behavior through the light bulb. This is important because if the switch has no resistance and there is voltage, then all the electricity flows through the switch if the switch is closed, or flows through the light bulb if the switch is open. In this case, an allow electricity behavior during the open mode through the light bulb only needs to be inferred.t This type of inference, which we call split bTference, needs to be performed for other kinds of causal patterns that involve parallel structures besides the parallel allow causal pattern, namely the parallel pump, parallel move, hlchtde expel, and pump move causal patterns. The latter two patterns, although they do not depend on the parallel structural relationship, imply behaviors that go through the same paths as one of their sub-behaviors, and consequently, there might be different behaviors on those paths during different modes. I

endt ILightBulb end2 A

I

B

end1J Switch Jend2 FIGURE 6. Switch and light bulb in parallel. t A similar inference is needed when the parallel allow causal pattern combines a one-way with a two-way allow behavior. The causal pattern can be used to combine the two behaviors for one direction, but a one-way behavior in the other direction needs to be inferred.

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2.Z4. Inferring the behaviors of the light bulb device Now consider the behaviors of the battery-switch (refer back to Section 2.2.2) and the light bulb in the device of Figure 1. The causal patterns allow the following behaviors to be inferred: Using circuit Using during

the serial allow causal pattern, an allow electricity behavior around the during the closed mode of the switch is inferred. the propagate pump causal pattern, a pump behavior around the circuit the closed mode is inferred.

Using the pump move causal pattern, a move behavior around the circuit during the closed mode is inferred. Using the parallel allow causal pattern, allow behaviors are inferred from the battery-switch connection (the connection between the battery and the switch) to the battery-light bulb connection (one part of the path goes through the battery and the other through the switch and the light bulb), from the battery-switch connection to the light bulb-switch connection, and from the light bulb-switch connection to the battery-light bulb connection. All the behaviors occur during the closed mode of the switch. Split inferences associated with the parallel allow inferences point out a number of behaviors. For each subpath that does not go through :the switch, a n allow behavior during the open mode of the switch is noted, e.g. an allow electricity behavior through the light bulb and battery during the open mode. Although the inferred behaviors listed in the last two items do not turn out to be important for the light bulb device, these behaviors might be important in different configurations. 2.2.5. Augmentations Although the types of behaviors and the causal patterns provide for the commonsense inference of a significant portion of device behavior, they lack the capability to handle two important aspects of behavior: the behavior of substances and dependencies between behaviors. We briefly discuss how our theory resolves these issues. In the light bulb example so far, it did not matter what substance was the object of the behaviors as long as certain behaviors acted upon the same substance. For example, consider the allow electricity behavior of the light bulb: allow electricity between end1 and end2 thru electrical There is more to say about this allow behavior than simply describing its path. The path has certain attributes that affect how electricity travels through it, such as resistance. In our theory, behaviors can be annotated with attributes and their values. Thus, one way that our theory differentiates between substances is through different attributes for each substance. Inferred behaviors will also have attributes, which will need to be computed based on the attributes of its sub-behaviors and the causal pattern used to infer the behavior. For example, the computation for electrical resistance will be different for the parallel allow and serial allow causal patterns. In our theory, each substance-causal pattern combination can be as-

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sociated with knowledge that specifies the appropriate computation. This provides another way that our theory differentiates between substances. Substance knowledge can also "retract" an inference, e.g. if two batteries of the same-Voltage oppose each other, then their combination will have no p u m p electricity behavior. An interaction crucial to the behavior of the light bulb, but not yet accounted for, is the relationship between the flow of electricity and the production of light. In our theory, the value of an attribute can be used to express how one behavior is dependent on other behaviors. For example, the create light behavior of the light bulb is proportional to the magnitude of movement of electricity through the ,light bulb. To express this dependency, the rate attribute of the create behavior can be described as: f(rate [move electricity f r o m endl to end2 thru electrical]))) where f computes the appropriate function. This dependency becomes satisfied when a m o v e electricity behavior is inferred around the light bulb circuit. In our theory, this inference is performed by "tracking" m o v e dependencies when behaviors are inferred with the causal patterns. First, the dependency is associated with the allow electricity of the light bulb. Since inference of a m o v e behavior requires an allow behavior, allow behaviors that move through the path of a m o v e dependency are the possible paths in which movement can affect the dependency. Second, when this allow behavior is used to infer additional allow behaviors, the dependency is associated with the inferred allow behaviors. In the case of a parallel allow inference, the fraction of flow going through the dependency needs to be computed. Finally, when a p u m p m o v e inference is made, the dependency is satisfied. 2.2. 6. Complexity and control

Remembering all the behaviors of the individual components as well as those inferred will present a combinatorial problem. For example, using the causal patterns exhaustively will result in a search of all paths in the device, including all parallel combinations of paths. To avoid these combinatorial problems, the consolidation process can be decomposed into consolidating two components at a time, and the behavioral descriptions of "composite components" can be summarized before further consolidation is performed. Thus, our algorithm for consolidation proceeds along the following steps: 1. Instantiate the device from the structural description of the device (e.g. Figure 5) and the behavioral descriptions of the components (e.g. Figures 2-4). 2. Choose two (composite) components to be a composite component. 3. Apply to the causal patterns to the behaviors of the chosen components, computing attributes and tracking dependencies as needed. 4. Summarize the behavioral description of the composite component. 5. Replace the chosen components with the new composite component. 6. Go back to Step 2 if there is more than one (composite) component left to be consolidated. 7. End. Our theory of consolidation provides for the summarization of a composite

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component in two ways. One, composite containers are created as combinations of containers and connections. Whenever a causal pattern is used to infer a behavior that goes through two or more structural elements, a composite container is derived from them. For example, the inferred allow behavior of the battery-switch would go through a composite container formed out of the electrical containers of the battery and switch, and the connection between the battery and switch. The inferred pump behavior goes through the same composite container as the inferred allow. There is one exception: if the inferred behavior is over a circuit, no composite container is made. If a composite container were made for a circuit, the direction of the behavior would be lost. Two, only those behaviors, containers, and connections that describe the external behavior of a composite component are selected for further consolidation. Many behaviors, containers, and connections of the composite component become irrelevant for describing how the composite component behaves with respect to the rest of the device. Those that are relevant become the external description of the composite component. For example, the allow electricity behavior of the switch is irrelevant to the external behavior of the battery-switch because the inferred allow and pump electricity behaviors incorporate all the useful information about the switch's allow behavior, i.e. useful for further reasoning about the overall behavior of the light bulb device. These two summarization features, composite containers and external description criteria, are sufficient to handle combinatorics of the number of paths. For one behavioral mode, a composite component has at most O(n 2) behaviors where n is number of open connections and individual (not composite) containers Of the composite component. There are O(n 2) pairs of places, and only a constant number of possible behaviors between each pair. For one behavioral mode, consolidating two components involves at most O(cn 2) inferences, where c is the number of connections between the components and n is again the number of open connections and individual containers. Usually the number of individual containers is not a key factor, but it is possible to each individual container to be important to the external behavior of the composite component. However, the number of behavioral modes can be still very large. If n components in a device each has 2 behavioral modes, then the device potentially has 2n behavioral modes. This points out a need to compose behavioral modes in some manner, but we have not determined how this can be done. We have not yet described how Step 2 of the algorithm--choosing components to be consolidated--can be performed. In complex devices, the choice can easily lead to an order of magnitude difference in the amount of work done. Our implementation constructs a "plan" of what pairs of components and composite components to consolidate, using one of two heuristics to reduce the open connections in the composite components. One heuristic is a top-down approach, i.e. divide the device into two subsystems with a minimal number of open connections between them, and recursively divide the subsystems. This is almost the same problem as minimal cut of a graph, which has a polynomial algorithm. A simpler heuristic is bottom-up-examine the possible pairs of components that can be consolidated, and choose the pair with the fewest open connections. With minor refinements, we implemented both heuristics, and both of them gave good results on the examples we modeled.

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2.2. 7. Consolidating the light bulb deoice We can now give a complete description of how our theory of consolidation infers that light is produced when the switch is closed. Suppose that the battery and the switch are first chosen for consolidation, followed by consolidation with the light bulb. This inference can proceed as follows (Figure 7 illustrates the inference of the creation of light from the behaviors of the components):

The allow electricity behaviors of the switch (box 1 in Figure 7) and battery (box 3) satisfy, the serial allow causal pattern, resulting in an allow electricity behavior through the battery and the switch (box 7). The resistance is the sum of the sub-behaviors' resistances. Because the switch's allow behavior is active only during the closed mode, so is the inferred behavior. The pump electricity behavior of the battery (box 2) and the allow electricity behavior of the switch (box 1) satisfy the propagate pump pattern, giving rise to a pump electricity behavior though the battery and the switch (box 6). The amount of influence is equal to the amount of the battery's pump electricity behavior. The behavior is active only during the closed mode. The allow electricity behaviors of the battery-switch (box 7) and light bulb (box 4) satisfy the serial allow pattern, resulting in an allow electricity behavior around the electrical circuit (box 9). The resistance is the sum of all the individual resistances. The behavior is active only during the closed mode.

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The p u m p electricity behavior of the battery-switch (box 6) and the allow electricity behavior of the light bulb (box 4) satisfy the p r o p a g a t e p t t m p pattern, from which a p u m p electricity behavior around the circuit is inferred (box 8). The amount of influence is equal to the amount of the battery-switch's p u m p electricity behavior. The behavior is active only during the closed mode. The previous two inferred behaviors (boxes 8 and 9) satisfy the p u m p m o v e causal pattern, so a m o v e behavior around the circuit is inferred (box 11). The rate of the move is a function of the resistance of the allow behavior and the amount of influence of the p u m p behavior. The behavior is active only during the closed mode. The m o v e electricity dependency in the create light behavior of the light bulb (dashed line from box 5 to box 4) is tracked from the allow electricity behavior of the light bulb to the allow electricity behavior around the circuit (dashed line from box 10 to box 9) to the m o v e electricity behavior around the circuit (dashed line from box 12 to box 11). This m o v e behavior satisfies the dependency expressed in the create light behavior of the light bulb (represented by a dark dashed line). The rate of light creation is proportional to the rate of electricity movement. This inference structure illustrated by Figure 7 can be directly used as an explanation of how this device produces light. This explanation provides a compositional causal account of the creation of light in the light bulb system in terms of the components' behaviors and the device's structure. The explanation is in terms of how the qualitative behavior of the components leads to the qualitative behavior of the device. The inference structure identifies the role of each component behavior by showing how they interact with each other to result in movement of electricity and creation of light. Simulation, qualitative or quantiative, is not necessary to produce this explanation.

3. Implemented examples Many other examples of devices have been studied and implemented in addition to the light bulb device. We briefly discuss several examples, all of which are discussed in detail in Bylander (1986). These examples are intended to give a feeling for the capabilities of our conceptual theory of consolidation. A few words about the implementation is in order. A general consolidation program was developed that selects components to consolidate, performs behavioral reasoning in accordance with the causal patterns, and calls upon knowledge of specific substances when appropriate. The program is implemented for a Xerox 1108 LISP machine, and is written in INTERLISP-D (Xerox, 1985), the language of the Xerox 1108, and in LOOPS (Bobrow & Stefik, 1982; Stefik et al., 1983), a software system that supports object-oriented programming in INTERLISP-D. For each example device, behavioral descriptions of the components, a structural description of the device, and substance knowledge to calculate attributes were specified. The main point to keep in mind is that the same consolidation machinery was used on all the examples, but that different knowledge was implemented for different components and substances.

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In the light bulb device used to illustrate the representation and reasoning in the previous sections, only the serial causal patterns played an important role. We studied another light bulb device in which all three components are parallel to each other (Figure 8). In this example, split inferences as discussed in Section 2.2.3 are needed. The problem is that when the switch is closed, it short circuits the battery, and no electricity goes through the light bulb; however, during the open mode of the switch, electricity does flow through the light bulb. The key inferences are made when the parallel allow causal pattern is used on the allow electricity behaviors of the light bulb and switch. An allow behavior through both components during the closed mode of the switch is inferred as expected, but because the light bulb's allow behavior is also active during the open mode, a split inference is used to derive an allow electricity behavior through the light bulb during the open mode. Now when the move dependency of the light bulb is tracked through these inferences, the fraction flowing through the light bulb will be 0 for the inferred Closed mode allow behavior and 1 during the open mode allow behavior. The rectifier in Figure 9 works in the following manner. A changing electromagnetic field through the coil (h~l and hz2 are the electromagnetic connections ) induces the coil to produce voltage between out1 and out2; half of this voltage is between out1 and middle and between middle and ottt2. Each diode only permits current to pass from end1 to end2, so the voltage due to the coil is from the middle connection of the coil to the junction of the diodes. This voltage causes the capacitor to store electricity, which is released by the resistor when the coil voltage is low, thus partially smoothing the voltage that the rectifier produces. The capacitor is modeled as having two electricity containers, which can have positive and negative "amounts" of electricity, and as having expel behaviors associated with the containers. During consolidation, a move electricity behavior is inferred between the containers of the capacitor through the coil and the diodes, accounting for the storage of electricity into the capacitor. Also, a move electricity behavior is inferred between the capacitor's containers through the resistor, accounting for the release of electricity. The refrigerator in Figure 10 works via two processes: a condensation/ evaporation cycle and maintenance of high pressure in the condenser and low

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pressure in the evaporator. The compressor pulls in and compresses the refrigerant from the evaporator, increasing the pressure (and thus the temperature) of the refrigerant; the refrigerant condenses in the condenser giving off heat in the process; the expansion valve decreases the pressure (and thus the temperature) of the refrigerant; and the refrigerant evaporates in the evaporator absorbing heat from the refrigerator box. This explanation makes assumptions about certain initial conditions, such as the amount of refrigerant and the outside temperature, so consolidation cannot duplicate this explanation. However, consolidation can infer a m o v e refrigerant behavior around the loop, and from that, a m o v e heat behavior between the condenser and the evaporator, and between the evaporator and the refrigerator box. The c a r r y m o v e causal pattern is used infer movement of heat from movement of refrigerant, i.e. because refrigerant contains heat, movement of refrigerant implies movement of heat. A model of the cardiovascular system in Figure 11 was developed in collaboration with Jack W. Smith, Jr. and John R. Svirbely (Bylander et al., 1988). More detailed models of the left heart and systemic circulation are also implemented. In the cardiovascular system, the right side of the heart moves blood into t h e pulmonary circulation, where the blood absorbs oxygen from and releases carbon dioxide into the lungs. The blood then flows to the left side of the heart, which pumps it into the systemic circulation, where the blood exchanges oxygen and carbon dioxide with the

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FIGURE12. Automobilegearbox. interstitium. The Cardiovascular Control component represents that part of the nervous system that regulates and synchronizes the other components. Only two open connections, to the lungs and the interstitium, are represented. Using consolidation, move blood behaviors are inferred between the components in the loop and because oxygen and carbon dioxide are contained in the blood, move oxygen and carbon dioxide behaviors between the lungs and interstitium are inferred. In addition, the behaviors of the left heart and systemic circulation are inferred from their components. Figure 12 illustrates the most complex device that we considered, a "simple" automobile gearbox with three forward gears and one reverse gear (taken from Heron, 1967, p. 490). To represent the meshed and unmeshed modes, a component called 3rd Gear Space with two behavioral modes is employed. The gears connecting the driving shaft to the lay shaft are used for first, second, and reverse gears. For example, second gear engages when a gear on the lay shaft meshes with a gear connected to the driven shaft. In this device, both first and reverse use the same gear on the driven shaft. Reverse gear uses an additional gear to accomplish a reversal in motion. The shift control, not shown in Figure 12, determines which, if any, of the gear spaces are closed. To do consolidation, we modeled rotational motion as a substance, i.e. in the gearbox, rotational motion is transferred from the driving shaft to the driven shaft. Our model had 16 components (gearshift, gears, shafts, "closeable" spaces between gears and shafts) with a total of 47 component behaviors. Our implementation matched the causal patterns 409 times to derive behaviors corresponding to neutral and the 4 gears as well as change mode behaviors that determine which gear is engaged.

4. Unresolved issues As promised in the introduction, the above account describes how consolidation can be performed conceptually, but does not provide a firm logical, psychological, or physical basis for consolidation. Clearly, further study of consolidation along these

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lines is indicated. For our own part, we are analysing how our theory can be reformulated in qualitative differential equations, the lingua franca of qualitative simulation. Of particular note is Dormoy and Raiman's (1988) technique for composing qualitative differential equations in certain cases. We are studying the relationship between their technique and the causal patterns. However, we do not expect a complete reduction of the causal patterns to qualitative differential equations because the behavior of each type of substance needs to be described with different constraints. Our theory does not cover all the concepts needed to do consolidation. One problem is that spatial descriptions are limited to connection and containment. To ft:lly represent and reason about the behavior of complex devices, the shapes and orientations of components need to be describable. It is important, e.g. to know that a piston of a car is shaped to fit inside a cylinder and to infer how the positions of the piston are constrained by the cylinder and other engine parts. Another problem with the structural primitives is that components cannot be a substance, and vice versa. Unfortunately, many objects play both roles. For example, a piston acts both as a component and a substance. In its component role, it is connected to other parts of the engine and transfers energy. As a substance, the piston can be acted upon, and can move from one place to another. Various aspects of reasoning about substances have been simplified in our theory. One simplification is that types of substances are not represented or reasoned about, e.g. water as a type of fluid. Another simplification is that mixtures of substances are not handled in a general way. Our theory can represent some mixtures as one substance containing another, but this is inadequate for reasoning about interactions between substances. The current summarization process incorporated within our theory (composite containers and external behavior description) does not always provide a concise and efficient behavioral description of the device. For example, the human heart has many behavioral modes (four chambers that contract and expand), different pump blood behaviors associated with each behavioral mode, interactions with the nervous system, etc. Often though, it is sufficient to describe the heart as just having a single pump blood behavior. Understanding how to approximate a complex set of behaviors with a single behavior would be a powerful adjunct to the causal patterns. The types of behaviors and causal patterns are incomplete in various ways. As mentioned above, actions at a distance such as gravity are not accounted for. Also, it would be useful to allow new types of behavior to be constructed. For example, a create behavior and a destroy behavior might be combined into a "transform" behavior. Finally, the construction of device models is subject to a great deal of subjectivity. For example, although a light bulb produces heat when it produces light, we chose not to represent this fact because reasoning about heat was not the point of the light bulb device example. While this omission was a matter of convenience as far as this example was concerned, it is important to avoid reasoning about phenomena that are extraneous to the situation. All components, for example, contain heat. All fluids can contain dissolved substances. All spaces allow material to move through them. However, no consolidation process (or any naive physics reasoning process for that matter) can afford to reason about all the physical phenomena and all the

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paths that can potentially interact in a situation. Explicitly recognizing these "omissions", deciding what to omit, and deciding when to retract an omission are all difficult problems for future research. Recent work in qualitative process theory is relevant to this problem (Falkenhainer & Forbus, 1988).

5. Conclusion We have presented a theory for inferring the behavioral description of a device by composing the behavioral descriptions of its components, i.e. a theory of consolidation. Our theory is based on a conceptual representatoin of behavior: primitive types of behaviors describe the behaviors of components and causal patterns infer the behaviors of the device. We have also discussed several examples on which our theory has been implemented and several extensions that are required before our theory can be used for reasoning about more complex physical situations. Consolidation is only one of the information processing tasks of naive physics. The discovery, elaboration, and integration of these tasks are important goals of naive physics research. Our primary contribution is a computational theory for performing consolidation in conceptual terms. I would like to acknowledge B. Chandrasekaran, Ken Forbus, John Josephson, Jordan Pollack, Jon Sticklen, and Mike Tanner for their comments on earlier drafts of this paper. Of course, this doesn't imply endorsement of the final product. This research was supported by the Air Force Office of Scientific Research through AFOSR Grants 82-0255 and 87-0090. The application to the cardiovascular system was supported by the National Library of Medicine through Grant LM 04298. Computer facilities were enhanced by giftS from Xerox Corporation.

References BOBROW, D. G. (1984). Qualitative reasoning about physical systems: an introduction. Artificial Intelligence, 24, 1-5. BonRow, D. G. & STEFIK, M. (1962). Tile LOOPS manual. Technical Report KB-VLSI-8113, Xerox Palo Alto Research Center. BYLANDER,T. (1986). Consolidation: a method for reasoning about the behaoiour of deoices. Ph.D. thesis, Laboratory for AI Research, CIS Department, Ohio State University, Columbus, OH. BYLANDER,T. (1988). A critique of qualitative simulation from a consolidation viewpoint. IEEE Transactions on Systems, Man, and Cybernetics, 18, 252-263. BYLANDER,T. (1990). Some causal models are deeper than others. Artificial Intelligence in Medichle, 2, 123-128. BYLANDER, Z. • CttANDRASEKARAN,B. (1985). Understanding behavior using consolidation. Proceedhlgs of the International Joint Conference on Artificial Intelligence, Los Angeles, pp. 450-454. BYLANDER, T., SMITtt, J. W., Jr. & SVIRBELY, J. R. (1988). Qualitative representation of behavior in the medical domain. Computers and Biomedical Research, 21, 367-380. DE KLEER, J. t~ BROWN, J. S. (1984). A qualitative physics based on confluences. Artificial Intelligence, 24, 7-83. DORMOY, J. (1988). Controlling qualitative resolution. Proceedings of the 7th National Conference on Artificial Intelligence, St. Paul, MN, pp. 319-323. DORMOY, J. & RA1MAN, O. (1988). Assembling a device. Artificial h~telligence in Engineering, 34, 216-226.

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FALKENHAINER, B. & FORBUS, K. D. (1988). Setting up large-scale qualitative models. Proceedings of the 7th National Conference on Artificial Intelligence, St. Paul, MN, pp. 301-306. FoRnus, K. D. (1984). Qualitative process theory. Artificial Intelligence, 24, 85-168. HAYES, P. J. (1985a). Naive physics I: Ontology for liquids. In J. R. Honns & R. C. MOORE, Eds. Formal Theories of the Commonsense World, pp. 71-107. Norwood, NJ: Ablex. HAYES, P. J. (1985b). The second naive physics manifesto. In J. R. Hoaas & R. C. MOORE, Eds. Formal Theories of the Commonsense World, pp. 1-36. Norwood, NJ: Ablex. HERON (1967). HOW Things Work. London: Heron Books. KUIPERS, B. J. (1986). Qualitative simulation. Artificial Intelligence, 29, 289-338. PATIL, R. S., SZOLOVITS,P. (~ SCtlWARTZ, W. B. (1982). Modeling knowledge of the patient in acid-base and electrolyte disorders. In P. SZOLOVITS, Ed. Artificial Intelligence in Medicine, pp. 191-226. Boulder, CO: Westview Press. POPLE, H. E. (1982). Heuristic methods for imposing structure on ill-structured problems: the structuring of medical diagnostics. In P. SZOLOVITS, Ed. Artificial Intelligence in Medicbze, pp. 119-190. Boulder, CO: Westview Press. RIEGER C. (~ GRINBERG, M. (1977). The declarative representation and procedural simulation of causality in physical mechanisms. Proceedings of the 5th btternational Joint Conference on Artificial bztelligence, Cambridge, ?viA, pp. 250-256. SC~tANK, R. C. (1975). Conceptual Information Processing. Amsterdam: North-Holland. STEFIK, M., BOBROW, D. G., MITI'AL, S. & CONWAY, L. (1983). Knowledge programming in LOOPS. AI Magazine, 4(3), 3-13. SUSSMAN, G. J. (~ STEELE, G. L. (1980). CONSTRAINTS--a language for expressing almost-hierarchical descriptions. Artificial Intelligence, 14, 1-39. WEISS, S. M., KULIKOWSKI,C. A., AMAREL, S. (~ SAFIR, A. (1978). A model-based method for computer-aided medical decision-making. Artificial Intelligence, 11, 145-172. WELD, D. S. (~ DE KLEER, J. Eds (1989). Readings in Qualitative Reasoning about Physical Systents. San MateD, CA: Morgan Kaufmann. WILKS, Y. (1973). An artificial intelligence approach to machine translation. In R. SO~ANK & K. COLBY, Eds. Computer Models of Thought and Language, pp. 114-151. San Francisco: W. H. Freeman. XEROX (1985). Interlisp Reference Manual. Pasadena, CA: Xerox Artificial Intelligence Systems.