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Exception hierarchies as a knowledge representation for expert systems
L O O K I N G A H E A D T O AI87 Selected Abstracts from The Second International Conference On Applications of AI in Engineering D . S r i r a m , M . I . T . a n d R. A d e y , C o m p u t a t i o n a l
Mechanics
T h e r e s p o n s e t o t h e s e c o n d i n t e r n a t i o n a l c o n f e r e n c e t o b e h e l d i n B o s t o n d u r i n g t h e first w e e k o f A u g u s t 1987 w a s overwhelming. We received over hundred abstracts, half of which were submitted from US and the other half came from all p a r t s o f t h e g l o b e . T h e p a p e r s c o v e r a w i d e r a n g e o f t o p i c s r a n g i n g f r o m d i a g n o s i s t o d e s i g n p r o b l e m s . I n a d d i t i o n t h e r e w e r e s e v e r a l c o n t r i b u t i o n s t h a t e m p h a s i z e d o n k n o w l e d g e r e p r e s e n t a t i o n t e c t m i q u e s a n d t o o l s . A r e p r e s e n t a t i v e set o f a b s t r a c t s is p r e s e n t e d i n t h i s i s s u e ; t h e p r o c e e d i n g s o f t h e c o n f e r e n c e will c o n t a i n t h e full l e n g t h p a p e r s .
From North America QUALITATIVE SKETCHING Elisha Sacks
MIT Laboratory for Computer Science This paper describes a qualitative sketcher, called QS, for parameterized families of real univariate functions. Qualitative sketching focuses on the high-level attributes of functions, such as extreme and discontinuites, without specifying their values at every point. At this level of abstraction, a few sketches generally suffice to describe entire families of parameterized functions. For example, the family fa(x)=e *x requires three sketches, shown in Fig. 1, corresponding to a negative, zero, and positive. QS helps its users understand the global behaviour of parameterized functions by generating all possible sketches. EXCEPTION HIERARCHIES AS A KNOWLEDGE REPRESENTATION FOR EXPERT SYSTEMS Rens¢ Lange
Department of Computer Science, University of Illinois at Urbana-Champaign Our own knowledge engineering experiences, as well as the results of psychological studies, indicates that experts tend to state general rules based on their personal experiences in one or more particular circumstances. Unfortunately however such rules tend to be too general and quickly lead to inconsistencies in the knowledge base. Nevertheless, broad rules reflect the experts" approach to solving a problem, and as such they provide an extremely useful framework for constructing a knowledge base. In order to take advantage of this framework, the question is therefore how to constrain the applicability of broad rules while maintaining their inherent simplicity and naturalness. In this paper a knowledge representation is proposed based on broad rules that can easily, and in a very intuitive manner, be qualified by possible (context dependent) exceptions. CONSTRAINT-BASED PROCESS PLANNING FOR PRECISION MACHINING Cornelius Nevrinceanu and Max Donath
University of Minnesota Manufacturing planning follows machine design in the process of product engineering. It is the activity by which, given the design specification of a mechanical component, a
plan of how to manufacture the component is devised. We have realized plan synthesis as constraint satisfaction in a constraint propagation network. Each technological feature has an associated holding constraint that materializes the degrees of freedom that need to be constrained in order to hold the feature for the purpose of machining. Holding constraints are connected by geometric constraints which control the propagation of holding uncertainty: propagation takes place only when the holding uncertainty is less than the allowable uncertainty imposed by the geometric constraints. Planning steps are generated as holding constraint satisfaction: as soon as a feature can be properly held, it can be machined and thus establishes itself in the chronological sequence of matching operations - the machining process plan. The planning system is implemented in a planning language synthesized by hierarchial object combination from a constraint language and from a mechanical component representation language. Planning language, constraint language, and mechanical component representation language are implemented in a object-oriented representation language imbedded in the ZetaLisp Flavor system.
BLACKBOARD APPROACH TO PROCESS PLANNING PROBLEMS A. Murphy, V. Jagannathan and S. Goodrum
Boeing Artificial Intelligence Center This research addresses the problem of whether blackboard systems provide a reasonable architecture to solve manufacturing process planning type problems. The framework used to study these issues is the Boeing Blackboard system (BBB) developed at the Boeing AI Center. BBB is a derivative of the BB1 system developed at Stanford. It is a tool for solving complex synthesis problems in constraint satisfaction, planning, and scheduling. It provides for opportunistic cooperation between multiple knowledge sources. The blackboard structure is essentially a shared memory facility providing a communication medium for multiple knowledge sources to react to the environment maintained in the blackboard. BBB has two generically different types of blackboard- control and domain. The domain blackboard manages domain-specific information and the control manages domainindependent and domain-specific control heuristics. This explicit separation of domain and control information allows for greater flexibility in directing the problem-solving
process. A knowledge source in BBB is a repository of information related to when and under what conditions its rules should be invoked. BBB is currently implemented on top of KEE.
ESTIMATOR: AN APPLICATION OF VARIABLE PRECISION LOGIC TO CONSTRUCTION COST ESTIMATION Peter Haddawy and Jim Kelly
Intelligent Systems Group, Department of Computer Science, University of Illinois The field of Artificial Intelligence has traditionally had a large gap between theoretically interesting research and useful 'real world" applications. In this paper we present the application of a new type of inference system, Variable Precision Logic (VPL), to the problem of estimating construction project costs.
KNOWLEDGE DIRECTED MICROARCHITECTURE DESIGN Forrest D. Brewer and Daniel D. Gajski
Department of Computer Science, University of Illinois at Urbana-Champaign Micro-architecture design starts with a behavioural specification of a digital system and produces a register-transfer level design which implements that behaviour. In addition this design must also satisfy several imposed constraints on design resources such as chip area, power requirements, and performance. This paper describes a new model ofthe design process and application of that model in 'Chippe' a new system for automated microarchitecture design. We model the design process as an iterative sequence of refinements, optimizations, and evaluations. Refinements are modifications to the global and local structural constraints of a candidate design. They allow direct implementation of design trade-offdecisions, adding knowledge to the design. Optimizations are constrained Searches for more efficient implementations of the design. They comprise modifications to the structure of the design or removal of redundant components. Lastly, evaluations are quality measures of the candidate design. These evaluations allow direct comparisons to be made between the design achievement and the required goals. Often families of related designs can be partitioned into design styles which have common attributes and design tradeoffs. This allows the partitioning of the design space by selection of a particular style. The design style
Artificial Intelligence in Engineering, 1987, Vol. 2, No. 2
119
Mechanics
T h e r e s p o n s e t o t h e s e c o n d i n t e r n a t i o n a l c o n f e r e n c e t o b e h e l d i n B o s t o n d u r i n g t h e first w e e k o f A u g u s t 1987 w a s overwhelming. We received over hundred abstracts, half of which were submitted from US and the other half came from all p a r t s o f t h e g l o b e . T h e p a p e r s c o v e r a w i d e r a n g e o f t o p i c s r a n g i n g f r o m d i a g n o s i s t o d e s i g n p r o b l e m s . I n a d d i t i o n t h e r e w e r e s e v e r a l c o n t r i b u t i o n s t h a t e m p h a s i z e d o n k n o w l e d g e r e p r e s e n t a t i o n t e c t m i q u e s a n d t o o l s . A r e p r e s e n t a t i v e set o f a b s t r a c t s is p r e s e n t e d i n t h i s i s s u e ; t h e p r o c e e d i n g s o f t h e c o n f e r e n c e will c o n t a i n t h e full l e n g t h p a p e r s .
From North America QUALITATIVE SKETCHING Elisha Sacks
MIT Laboratory for Computer Science This paper describes a qualitative sketcher, called QS, for parameterized families of real univariate functions. Qualitative sketching focuses on the high-level attributes of functions, such as extreme and discontinuites, without specifying their values at every point. At this level of abstraction, a few sketches generally suffice to describe entire families of parameterized functions. For example, the family fa(x)=e *x requires three sketches, shown in Fig. 1, corresponding to a negative, zero, and positive. QS helps its users understand the global behaviour of parameterized functions by generating all possible sketches. EXCEPTION HIERARCHIES AS A KNOWLEDGE REPRESENTATION FOR EXPERT SYSTEMS Rens¢ Lange
Department of Computer Science, University of Illinois at Urbana-Champaign Our own knowledge engineering experiences, as well as the results of psychological studies, indicates that experts tend to state general rules based on their personal experiences in one or more particular circumstances. Unfortunately however such rules tend to be too general and quickly lead to inconsistencies in the knowledge base. Nevertheless, broad rules reflect the experts" approach to solving a problem, and as such they provide an extremely useful framework for constructing a knowledge base. In order to take advantage of this framework, the question is therefore how to constrain the applicability of broad rules while maintaining their inherent simplicity and naturalness. In this paper a knowledge representation is proposed based on broad rules that can easily, and in a very intuitive manner, be qualified by possible (context dependent) exceptions. CONSTRAINT-BASED PROCESS PLANNING FOR PRECISION MACHINING Cornelius Nevrinceanu and Max Donath
University of Minnesota Manufacturing planning follows machine design in the process of product engineering. It is the activity by which, given the design specification of a mechanical component, a
plan of how to manufacture the component is devised. We have realized plan synthesis as constraint satisfaction in a constraint propagation network. Each technological feature has an associated holding constraint that materializes the degrees of freedom that need to be constrained in order to hold the feature for the purpose of machining. Holding constraints are connected by geometric constraints which control the propagation of holding uncertainty: propagation takes place only when the holding uncertainty is less than the allowable uncertainty imposed by the geometric constraints. Planning steps are generated as holding constraint satisfaction: as soon as a feature can be properly held, it can be machined and thus establishes itself in the chronological sequence of matching operations - the machining process plan. The planning system is implemented in a planning language synthesized by hierarchial object combination from a constraint language and from a mechanical component representation language. Planning language, constraint language, and mechanical component representation language are implemented in a object-oriented representation language imbedded in the ZetaLisp Flavor system.
BLACKBOARD APPROACH TO PROCESS PLANNING PROBLEMS A. Murphy, V. Jagannathan and S. Goodrum
Boeing Artificial Intelligence Center This research addresses the problem of whether blackboard systems provide a reasonable architecture to solve manufacturing process planning type problems. The framework used to study these issues is the Boeing Blackboard system (BBB) developed at the Boeing AI Center. BBB is a derivative of the BB1 system developed at Stanford. It is a tool for solving complex synthesis problems in constraint satisfaction, planning, and scheduling. It provides for opportunistic cooperation between multiple knowledge sources. The blackboard structure is essentially a shared memory facility providing a communication medium for multiple knowledge sources to react to the environment maintained in the blackboard. BBB has two generically different types of blackboard- control and domain. The domain blackboard manages domain-specific information and the control manages domainindependent and domain-specific control heuristics. This explicit separation of domain and control information allows for greater flexibility in directing the problem-solving
process. A knowledge source in BBB is a repository of information related to when and under what conditions its rules should be invoked. BBB is currently implemented on top of KEE.
ESTIMATOR: AN APPLICATION OF VARIABLE PRECISION LOGIC TO CONSTRUCTION COST ESTIMATION Peter Haddawy and Jim Kelly
Intelligent Systems Group, Department of Computer Science, University of Illinois The field of Artificial Intelligence has traditionally had a large gap between theoretically interesting research and useful 'real world" applications. In this paper we present the application of a new type of inference system, Variable Precision Logic (VPL), to the problem of estimating construction project costs.
KNOWLEDGE DIRECTED MICROARCHITECTURE DESIGN Forrest D. Brewer and Daniel D. Gajski
Department of Computer Science, University of Illinois at Urbana-Champaign Micro-architecture design starts with a behavioural specification of a digital system and produces a register-transfer level design which implements that behaviour. In addition this design must also satisfy several imposed constraints on design resources such as chip area, power requirements, and performance. This paper describes a new model ofthe design process and application of that model in 'Chippe' a new system for automated microarchitecture design. We model the design process as an iterative sequence of refinements, optimizations, and evaluations. Refinements are modifications to the global and local structural constraints of a candidate design. They allow direct implementation of design trade-offdecisions, adding knowledge to the design. Optimizations are constrained Searches for more efficient implementations of the design. They comprise modifications to the structure of the design or removal of redundant components. Lastly, evaluations are quality measures of the candidate design. These evaluations allow direct comparisons to be made between the design achievement and the required goals. Often families of related designs can be partitioned into design styles which have common attributes and design tradeoffs. This allows the partitioning of the design space by selection of a particular style. The design style
Artificial Intelligence in Engineering, 1987, Vol. 2, No. 2
119