- Email: [email protected]
An agent-based framework for agricultural knowledge based systems
Copyright © 2004 IFAC Fifth International Workshop on Artiticiallntelligencc in Agriculture, Cairo, Egypt
An Agent-Based Framework for Agricultural Knowledge Based Systems AhmedKamel
Computer Science Department North Dakota State University Fargo. North Dakota 58105 USA [email protected]
Abstract: This paper proposes an architecture for cooperative problem solving utilizing features from both knowledge -based systems technology and intelligent agents technology. By redefining knowledge-based systems as individual autonomous agents with well-defined capabilities, tremendous flexibility can be achieved. Complex problem solvers can be assembled from individual agents. These problem solvers would have capabilities that far exceed the sum of the capabilities of the individual agents. By thinking of the agents in terms of their advertised capabilities regardless of their physical location, this approach enables the use of geographically dispersed agents to cooperatively solve complex problems. Copyright © 2002 IFAC Keywords: Agents, Agriculture, Knowledge -based systems.
I. INTRODUCTION
Two problem -solving technologies have undergone tremendous development in recent years: the relatively maturing knowledge-based systems technology and the relatively new and evolving Intelligent Agents technology. Knowledge-based systems technology is a relatively maturing technology with several successful paradigms currently in widespread use. While these methods have been successfully applied in numerous domains (such as industrial design, agricultural planning, financial planning, ...etc.), most knowledge-based systems havc been characterized as being "standalone" applications that can only be executed on the computer in which they reside together \vith their data. This restriction has always been a bottleneck that hinders the \videspread commercial use of knowledge-based systems. By redefining knowledge based systems as individual autonomous agents with well-defmed capabilities, tremcndous flexibility can be achieved. Complex problem solvers can be assembled from individual agents. These problem solvers would have capabilities that far exceed the sum . of the capabilities of the individual agents.
6
Furthermore, recent developments in Computer Networks and the widespread use of the Internet enables us to think of these agents as not being bound to any specific location. We only need to think of the agents in terms of their advertised capabilities regardless of their physical location. This approach enables the use of geographically dispersed agents to cooperatively solve complex problems. Additionally, this enables a more systematic approach for system maintenance and updates since every agent is located in only one location. Furthermore by defining a standard for collaboration, numerous repetitive tasks that are necessary for multiple systems can be defired as standardized agents that are called upon to provide services to these multiple systems. As an example, consider the task of obtaining futures prices. Any agricultural planning system would benefit from this capability. Instead of consistently duplicating this capability across different systems, by defIDing it as a stand-alone agent, we can ensure its availability to the different systems that need it. The same argument can be applied to other capabilities such as weather forecasts, commodities prioes, .. .etc. By providing a standardized interface, and a communication language, agents providing
these capabilities can be published and made available for use by other agent systems in a fashion similar .10 the use of web services. This paper presents a proposed architecture for cooperntive problem solving utilizing featW'eS from both knowledge-based systems technology and intelligent agents technology.
3.1 Background of2 Key Generic Tasks
In our overall problem solving architecture, we integrnte a number of Generic Tasks (GT' sTwo GT problem-solving types are presented here; hiernrchical classification (By1ander and Mittal 1986; Sticklen,' Chandrasekaran et al. 1987) and routine design (Brown and Chandmsekaran 1989; Karnel, McDowell et al. 1994).
2. PROBLEM DEFINITION We previously developed an integrnted system for the management of irrigated wheat in Egypt (Kamel, Sticklen et al. 1997; Kamel, Sticklen et al. 2(00). This system in reality is a collection of collaborating problem solvers, each charged with addressing a specific aspect of the wheat management problem (e.g. varietal selection, ungation management, fertilization management, ... etc). The integIation of these systems is implemented in a "hard-wired" fashion. As a result, maintenance and updates to one component inevitably affects severnl other components. We view these systems as a prototype that is genera1izable to other domains (in agriculture and otherwise). To accomplish this goal, severnl enhancement to the CI:rrent mode of operation need to be enacted:
Hierarchical classification is intuitively a knowledge organi7ation and control technique for selecting among a nwnber of hierarchically organized options. As a selection methodology, it is readily WIderstood, and will not be further described here. For more detail on hierarchical classification, please consult the cited reterences above. We also developed an extension for hierarchical classification in a previous project to select based on visual piClW'eS as described ill (Schulthess, Schroeder et al. 1994; Schulthess. Ward et al. 1994; Schulthess, Schroeder et al. 1996). Routine design however is not as intuitive and will be described in more detail here. The Routine Design GT forms a high level planning
•
A mechanism is needed to allow the individual problem solvers to act . individually on their respective problems without regard to other problem solvers. Integrntion of the results would then be accomplished in a trnnsparent fashion.
template for the generation of design assemblies as well as for the generntion of plans of actions. We have previously used Routine Design as the backbone of an engineering design system (Kamel 1994) as well as for an agriculturnl planning system (Kamel, Schroeder et al. 1995).
•
Due to the static, stand -alone nature of the current system, all data input operntions are performed through querying the user even if the data is easily obIainable through other means (the Internet, databases, ... etc). A useful additional would be to allow the system to acquire any necessary data input automatically if when possible.
The basic intuition underlying Routine Design is that a successful Artificial Intelligence teclmique should follow the same method of reasoning as humans do to be able to use predictions to form hypotheses about how a hwnan. designer woukl behave in situations that have not been observed or ana1yzed and act accordingly. A consequence that follows from this intuition is the use of hierarchical structures of design specialists to perform design, each responsible for a particular part of the overall plan. Hierarchies are used not because the design is intrinsically hierarchical, but because hierarchical decomposition is a typical means utilized to manage complexity.
The rest of this paper presents a proposed architecture utilizing the technology from Intelligent software agents to allow the development of knowledge based systems that act as individual agents charged with specific problems. An additional coordination agent will also need to be developed to coordinate and integrnte the efforts of the individual agent problem solvers.
The input is a set of planning constraints, and the output should be a full set of specifications for the required plan. The information -processing task can be summarized as follows and as shown diagrammatically in Figure I:
3. BACKGROUND Since this work builds closely on our knowledgesystems research for the management of irrigated wheat, this section provides an overview of the knowledge-based systems teclmiques we use (The Generic Task a!'Proach (Chandrasekaran 1986) as well as our iJrigated wheat management application. (Kamel, Schroeder et al. 1993; Kamel, Schroeder et al. 1995; Karnel, Sticklen et al. 2(00). lxIsed
I.
Working on a problem that has been done many [ DeSign . ~ Problem Design - { Ocs'Ign ] Constraints Solver SpecifICAtions
Figure I: Information Routine Design
7
Processing
Task
of
times before. each time with different but similar requirements. until the problem solving knowledge has been compiled into a form that allows efficient solution of the problem. and
i
Design proceeds "ith each sub-problem by selecting from previously known sets of wellundenaood alternatives.
3.2 NEPER Wheat Previous research in the Generic Task Approach typically focused on applying one generic task to one problem. On a previous project, we applied an integrated problem solving architecture for planning a cropping season for an inigated wheat farm. Analysis of the problem of inigated wheat management reveals a multi-task problem with several different task types needed to address the problem. Therefore. we found a nced to integrate multiple GT's into one problem solver.
FigLD"e 2: Strategic Planning Module
subspeciaJists (Varietal Selection, Planting Date, Strategic Pest Management, Preplant Tillage, Planting Parameters, FertilizerlWater Regime, and Harvest Specialists) to perform the task of plan generation.
4. AGENT ARCHITECTURE Figure 3 shows a high-level overview of the system. At the top level, our system is composed of two cooperating agents, the Strategic Planner, and CERES Wheat, a wheat modelling system(Ritchie, Godwin et al. 1985} The strategi<; planning module uses compiled knowledge of wheat crop management to propose a plan, consulting with the CERES wheat simulation model in the process.
rZ------,
In this section, a unified agent architecture to address the 2 problems discussed in section 2 above (individual problem solvers as independent agents, and autornatic data acquisition) is presented. The architecture relies on a central agent acting as a coordinator for available services. Agents register their capabilities with this coordinator agent. Other agents need only know of the existence of the coordinator agent When any agent requires a service. it requests it from the central agent. The central agent then delegates the task to any agent with the desired capability. If no agent possesses this capability, the coordination agent informs the requesting agent of this fact.
Season Plan
In this architecture, individual problem solvers (such as individJal modules from an agricultural planning the system) register their capabilities with coordination agent The overall planning system would then access these capabilities through the coordination agent. Furthermore, a collection of simple agents need to re built to address the need for automatic data acquisition for data values known to exist in electronic forms on the Internet or in known databases. These agents would then serve as generalpurpose data seekers to be utilized by problemsolving agents. Generic problem-rolving agents can also be developed to solve standard problems representing repetitive tasks that other problemsolving agents require (such as computing areas. figuring out treatments for specific diseases or pests. .. .etc.).
Figure 3: Top Level View of Wheat Management System
3.2.1 Strategic Planning Module From an expert systems viewpoint. the heart of our system lies in the Strategic Planning Module. TIle role of the strategic pl:mning module is to generate a plan for the management of a wheat crop for an entire cropping season. To design this planning module, we followed the Generic Task approach. At the highest level, the task of creating a strategic plan is seen as a design problenl - designing a plan for the management of wheat. Thus, a Routine Design problem solver is incorporated as the top-level problem-solving agent.
4. J Three- Tier Agent Architecture
FigLD"e 2 depicts the top level architecture of the strategic planning module. The top specialist, (Strategic Planning Module) makes use of the
In order to hide the internal details of individual agents from other agents, a 'Three tier Agent
8
•
With this architecture the user does not need to learn the way in which the individual Interne! services have to be operated.
5. CONCLUSION
Requesting KBS
This paper presents a Wlified agent architecture that can be utilized for implementing agricultural knowledge based systems. By implementing the basic fimctionalities conunon to agricultural systems as. individual agents and registering their capabilities WIth a centralized coordinator. developers of agricultural management systems can focus on the task on hand, and for other routine tasks, they can focus on "what" tasks need to be accomplished instead of "how" to accomplish them.
Figure 4: 'fhreo.Tier Architecture
Architecture" is proposed as shown in Figure 4. TIle 3 tiers include: I.
The Requestor (of data or information): The information seeker is a Knowledge based system. A Software agent is tasked with finding out exactly what a KBS is looking for. In this layer, we have stationary agents, having the capability to find out exactly what the KBS is looking for and then passing that information to retrieval agents, which are waiting for invocation from stationary agents. After understanding the requirements of the KBS, the retrieval agents request the required information from the coordination agent.
2
The coordination agent: This agent receives the requests and delegates them to other agents whose capabilities have been previously registered with the coordination agent.
3.
ITovider agents: These are the individual problem solvers whose capabilities have previously been registered with the coordination
4. 2 Adl'Qlllages oJrhe 77l1"1?e-Tier Architecture The proposed architecture has several advantages:
•
Each of the. ~ . layers specializes in only one task. thus smlphfymg the design process of the individual agents at each level.
•
The Architecture does not enforce a specific type of software or hardware. This means that di ffe.rent underlying techniques (such as agent architecture or programming 1anguage) may be used to create the agents, as long as all agents agree on a common inter-agent comrmmication protocol.
REFERENCES Brown. D. and B. Chandrasekaran (1989). Design Probl.:m Solving: Knowledge Structures and Control Strategies . London. Pitrnan. Bylander. T. and S. Mitta1 (1986). ''CSRL: A Language for C1assificatory Problem Solving and Uncertainty Handling." AI Magazine 7(2): 66-77. Chandrasekaran, B. (1986). "Generic Tasks in Knowledge-Based Reasoning: .High-LeveI Building Blocks for Expert System Design." IEEE Expert l(fall): ~30. Kamel, A. (1994). Generating Multiple Design Alternatives of Composite Materials using a Generic-Task Approach., Michigan State University. Kamel, A., J. McDowell and J. Sticklen (1994). M u1tiple Design: An Extension of Routine Design for Generating Multiple Design Alternatives . Artificial Intelligence in Design '94, Lausanne, Switzerland, K1uwer Academic Publishers. Kamel, A., K. Schroeder and J. Stick\en (1993). An Integrated Wheat Crop MlIl8gement System Based on Generic Task Knowledge Based Systents and CERES Nwnerical Simulation. IJCAl-93, Workshop on Artificial Intelligence in Agriculture and Natural Resources. France. Kamel, A., K. Schroeder and J. Sticklen ( 1995). "Integrated Wheat Crop Management Based on Generic Task Knowledge Based Systems and CERES Nwnerical Simulation." AI Applications 9(1): 17-28. Kamel, A., J. Sticklen and A. Rafea (2000). NEPERWheat: Integrated Problem Solving Architecture for Crop Management. 13th International Conference on Software & Systems Engineering and their Applications, Paris, France. Kamel, A., J. Sticklen, A. Rafea. U. Sculthess, J. Ritchie and R. Ward (1997). Integrating Numerical Models with Task Specific Architectures for Crop Management in
9
Production Agriculture Resowre Teclmology 97, Seattle, Washington. Ritchie, J. T., D. C. Godwin and S. Otter-Nacke (1985). CERES Wheat: A Simulation Model of Wheat Growth and Development. College Station, Texas, Texas A&M University Press. sChulthess, U., K. Sdtroeder, A. Kamel, E. E. Hassanein, A. H. S. Shaban, A. A. EI-Ghani, A. A. EI-Shafy, J. Ritchie, R. Ward and J. Sticklen (1996). "NEPER-Weed: a picture-based expert system for weed identification." Agronomy ~3):
423-427.
Schulthess, U., K. Schroeder, A. Kamel, J. Sticklen, R. Ward. J. Ritchie, A. Rafea, A. Salah and A. S. Ali (1994). Weed Identification Using a PictureBased Hierarchical Classification System. AAAI Workshop on Artificial Intelligence in Agriculture and Natural Resource Management, Seattle, Washington, AAAI. Schulthess, U., R. Ward, K. Scroeder, A. Kame1, J. Sticklen, A. A. S. Ali and A,G. M. Abdel-Ghani (1994). Weed Identification Using a PictureBased Hierarchical Classification System . Decision Support-200I, Toronto, Canada. Sticklen, J., B. Chandrasekaran and J. Josephson (1987). "ModuIarity of Domain Knowledge." Expert Systems: Research and Applications 1.
10
An Agent-Based Framework for Agricultural Knowledge Based Systems AhmedKamel
Computer Science Department North Dakota State University Fargo. North Dakota 58105 USA [email protected]
Abstract: This paper proposes an architecture for cooperative problem solving utilizing features from both knowledge -based systems technology and intelligent agents technology. By redefining knowledge-based systems as individual autonomous agents with well-defined capabilities, tremendous flexibility can be achieved. Complex problem solvers can be assembled from individual agents. These problem solvers would have capabilities that far exceed the sum of the capabilities of the individual agents. By thinking of the agents in terms of their advertised capabilities regardless of their physical location, this approach enables the use of geographically dispersed agents to cooperatively solve complex problems. Copyright © 2002 IFAC Keywords: Agents, Agriculture, Knowledge -based systems.
I. INTRODUCTION
Two problem -solving technologies have undergone tremendous development in recent years: the relatively maturing knowledge-based systems technology and the relatively new and evolving Intelligent Agents technology. Knowledge-based systems technology is a relatively maturing technology with several successful paradigms currently in widespread use. While these methods have been successfully applied in numerous domains (such as industrial design, agricultural planning, financial planning, ...etc.), most knowledge-based systems havc been characterized as being "standalone" applications that can only be executed on the computer in which they reside together \vith their data. This restriction has always been a bottleneck that hinders the \videspread commercial use of knowledge-based systems. By redefining knowledge based systems as individual autonomous agents with well-defmed capabilities, tremcndous flexibility can be achieved. Complex problem solvers can be assembled from individual agents. These problem solvers would have capabilities that far exceed the sum . of the capabilities of the individual agents.
6
Furthermore, recent developments in Computer Networks and the widespread use of the Internet enables us to think of these agents as not being bound to any specific location. We only need to think of the agents in terms of their advertised capabilities regardless of their physical location. This approach enables the use of geographically dispersed agents to cooperatively solve complex problems. Additionally, this enables a more systematic approach for system maintenance and updates since every agent is located in only one location. Furthermore by defining a standard for collaboration, numerous repetitive tasks that are necessary for multiple systems can be defired as standardized agents that are called upon to provide services to these multiple systems. As an example, consider the task of obtaining futures prices. Any agricultural planning system would benefit from this capability. Instead of consistently duplicating this capability across different systems, by defIDing it as a stand-alone agent, we can ensure its availability to the different systems that need it. The same argument can be applied to other capabilities such as weather forecasts, commodities prioes, .. .etc. By providing a standardized interface, and a communication language, agents providing
these capabilities can be published and made available for use by other agent systems in a fashion similar .10 the use of web services. This paper presents a proposed architecture for cooperntive problem solving utilizing featW'eS from both knowledge-based systems technology and intelligent agents technology.
3.1 Background of2 Key Generic Tasks
In our overall problem solving architecture, we integrnte a number of Generic Tasks (GT' sTwo GT problem-solving types are presented here; hiernrchical classification (By1ander and Mittal 1986; Sticklen,' Chandrasekaran et al. 1987) and routine design (Brown and Chandmsekaran 1989; Karnel, McDowell et al. 1994).
2. PROBLEM DEFINITION We previously developed an integrnted system for the management of irrigated wheat in Egypt (Kamel, Sticklen et al. 1997; Kamel, Sticklen et al. 2(00). This system in reality is a collection of collaborating problem solvers, each charged with addressing a specific aspect of the wheat management problem (e.g. varietal selection, ungation management, fertilization management, ... etc). The integIation of these systems is implemented in a "hard-wired" fashion. As a result, maintenance and updates to one component inevitably affects severnl other components. We view these systems as a prototype that is genera1izable to other domains (in agriculture and otherwise). To accomplish this goal, severnl enhancement to the CI:rrent mode of operation need to be enacted:
Hierarchical classification is intuitively a knowledge organi7ation and control technique for selecting among a nwnber of hierarchically organized options. As a selection methodology, it is readily WIderstood, and will not be further described here. For more detail on hierarchical classification, please consult the cited reterences above. We also developed an extension for hierarchical classification in a previous project to select based on visual piClW'eS as described ill (Schulthess, Schroeder et al. 1994; Schulthess. Ward et al. 1994; Schulthess, Schroeder et al. 1996). Routine design however is not as intuitive and will be described in more detail here. The Routine Design GT forms a high level planning
•
A mechanism is needed to allow the individual problem solvers to act . individually on their respective problems without regard to other problem solvers. Integrntion of the results would then be accomplished in a trnnsparent fashion.
template for the generation of design assemblies as well as for the generntion of plans of actions. We have previously used Routine Design as the backbone of an engineering design system (Kamel 1994) as well as for an agriculturnl planning system (Kamel, Schroeder et al. 1995).
•
Due to the static, stand -alone nature of the current system, all data input operntions are performed through querying the user even if the data is easily obIainable through other means (the Internet, databases, ... etc). A useful additional would be to allow the system to acquire any necessary data input automatically if when possible.
The basic intuition underlying Routine Design is that a successful Artificial Intelligence teclmique should follow the same method of reasoning as humans do to be able to use predictions to form hypotheses about how a hwnan. designer woukl behave in situations that have not been observed or ana1yzed and act accordingly. A consequence that follows from this intuition is the use of hierarchical structures of design specialists to perform design, each responsible for a particular part of the overall plan. Hierarchies are used not because the design is intrinsically hierarchical, but because hierarchical decomposition is a typical means utilized to manage complexity.
The rest of this paper presents a proposed architecture utilizing the technology from Intelligent software agents to allow the development of knowledge based systems that act as individual agents charged with specific problems. An additional coordination agent will also need to be developed to coordinate and integrnte the efforts of the individual agent problem solvers.
The input is a set of planning constraints, and the output should be a full set of specifications for the required plan. The information -processing task can be summarized as follows and as shown diagrammatically in Figure I:
3. BACKGROUND Since this work builds closely on our knowledgesystems research for the management of irrigated wheat, this section provides an overview of the knowledge-based systems teclmiques we use (The Generic Task a!'Proach (Chandrasekaran 1986) as well as our iJrigated wheat management application. (Kamel, Schroeder et al. 1993; Kamel, Schroeder et al. 1995; Karnel, Sticklen et al. 2(00). lxIsed
I.
Working on a problem that has been done many [ DeSign . ~ Problem Design - { Ocs'Ign ] Constraints Solver SpecifICAtions
Figure I: Information Routine Design
7
Processing
Task
of
times before. each time with different but similar requirements. until the problem solving knowledge has been compiled into a form that allows efficient solution of the problem. and
i
Design proceeds "ith each sub-problem by selecting from previously known sets of wellundenaood alternatives.
3.2 NEPER Wheat Previous research in the Generic Task Approach typically focused on applying one generic task to one problem. On a previous project, we applied an integrated problem solving architecture for planning a cropping season for an inigated wheat farm. Analysis of the problem of inigated wheat management reveals a multi-task problem with several different task types needed to address the problem. Therefore. we found a nced to integrate multiple GT's into one problem solver.
FigLD"e 2: Strategic Planning Module
subspeciaJists (Varietal Selection, Planting Date, Strategic Pest Management, Preplant Tillage, Planting Parameters, FertilizerlWater Regime, and Harvest Specialists) to perform the task of plan generation.
4. AGENT ARCHITECTURE Figure 3 shows a high-level overview of the system. At the top level, our system is composed of two cooperating agents, the Strategic Planner, and CERES Wheat, a wheat modelling system(Ritchie, Godwin et al. 1985} The strategi<; planning module uses compiled knowledge of wheat crop management to propose a plan, consulting with the CERES wheat simulation model in the process.
rZ------,
In this section, a unified agent architecture to address the 2 problems discussed in section 2 above (individual problem solvers as independent agents, and autornatic data acquisition) is presented. The architecture relies on a central agent acting as a coordinator for available services. Agents register their capabilities with this coordinator agent. Other agents need only know of the existence of the coordinator agent When any agent requires a service. it requests it from the central agent. The central agent then delegates the task to any agent with the desired capability. If no agent possesses this capability, the coordination agent informs the requesting agent of this fact.
Season Plan
In this architecture, individual problem solvers (such as individJal modules from an agricultural planning the system) register their capabilities with coordination agent The overall planning system would then access these capabilities through the coordination agent. Furthermore, a collection of simple agents need to re built to address the need for automatic data acquisition for data values known to exist in electronic forms on the Internet or in known databases. These agents would then serve as generalpurpose data seekers to be utilized by problemsolving agents. Generic problem-rolving agents can also be developed to solve standard problems representing repetitive tasks that other problemsolving agents require (such as computing areas. figuring out treatments for specific diseases or pests. .. .etc.).
Figure 3: Top Level View of Wheat Management System
3.2.1 Strategic Planning Module From an expert systems viewpoint. the heart of our system lies in the Strategic Planning Module. TIle role of the strategic pl:mning module is to generate a plan for the management of a wheat crop for an entire cropping season. To design this planning module, we followed the Generic Task approach. At the highest level, the task of creating a strategic plan is seen as a design problenl - designing a plan for the management of wheat. Thus, a Routine Design problem solver is incorporated as the top-level problem-solving agent.
4. J Three- Tier Agent Architecture
FigLD"e 2 depicts the top level architecture of the strategic planning module. The top specialist, (Strategic Planning Module) makes use of the
In order to hide the internal details of individual agents from other agents, a 'Three tier Agent
8
•
With this architecture the user does not need to learn the way in which the individual Interne! services have to be operated.
5. CONCLUSION
Requesting KBS
This paper presents a Wlified agent architecture that can be utilized for implementing agricultural knowledge based systems. By implementing the basic fimctionalities conunon to agricultural systems as. individual agents and registering their capabilities WIth a centralized coordinator. developers of agricultural management systems can focus on the task on hand, and for other routine tasks, they can focus on "what" tasks need to be accomplished instead of "how" to accomplish them.
Figure 4: 'fhreo.Tier Architecture
Architecture" is proposed as shown in Figure 4. TIle 3 tiers include: I.
The Requestor (of data or information): The information seeker is a Knowledge based system. A Software agent is tasked with finding out exactly what a KBS is looking for. In this layer, we have stationary agents, having the capability to find out exactly what the KBS is looking for and then passing that information to retrieval agents, which are waiting for invocation from stationary agents. After understanding the requirements of the KBS, the retrieval agents request the required information from the coordination agent.
2
The coordination agent: This agent receives the requests and delegates them to other agents whose capabilities have been previously registered with the coordination agent.
3.
ITovider agents: These are the individual problem solvers whose capabilities have previously been registered with the coordination
4. 2 Adl'Qlllages oJrhe 77l1"1?e-Tier Architecture The proposed architecture has several advantages:
•
Each of the. ~ . layers specializes in only one task. thus smlphfymg the design process of the individual agents at each level.
•
The Architecture does not enforce a specific type of software or hardware. This means that di ffe.rent underlying techniques (such as agent architecture or programming 1anguage) may be used to create the agents, as long as all agents agree on a common inter-agent comrmmication protocol.
REFERENCES Brown. D. and B. Chandrasekaran (1989). Design Probl.:m Solving: Knowledge Structures and Control Strategies . London. Pitrnan. Bylander. T. and S. Mitta1 (1986). ''CSRL: A Language for C1assificatory Problem Solving and Uncertainty Handling." AI Magazine 7(2): 66-77. Chandrasekaran, B. (1986). "Generic Tasks in Knowledge-Based Reasoning: .High-LeveI Building Blocks for Expert System Design." IEEE Expert l(fall): ~30. Kamel, A. (1994). Generating Multiple Design Alternatives of Composite Materials using a Generic-Task Approach., Michigan State University. Kamel, A., J. McDowell and J. Sticklen (1994). M u1tiple Design: An Extension of Routine Design for Generating Multiple Design Alternatives . Artificial Intelligence in Design '94, Lausanne, Switzerland, K1uwer Academic Publishers. Kamel, A., K. Schroeder and J. Stick\en (1993). An Integrated Wheat Crop MlIl8gement System Based on Generic Task Knowledge Based Systents and CERES Nwnerical Simulation. IJCAl-93, Workshop on Artificial Intelligence in Agriculture and Natural Resources. France. Kamel, A., K. Schroeder and J. Sticklen ( 1995). "Integrated Wheat Crop Management Based on Generic Task Knowledge Based Systems and CERES Nwnerical Simulation." AI Applications 9(1): 17-28. Kamel, A., J. Sticklen and A. Rafea (2000). NEPERWheat: Integrated Problem Solving Architecture for Crop Management. 13th International Conference on Software & Systems Engineering and their Applications, Paris, France. Kamel, A., J. Sticklen, A. Rafea. U. Sculthess, J. Ritchie and R. Ward (1997). Integrating Numerical Models with Task Specific Architectures for Crop Management in
9
Production Agriculture Resowre Teclmology 97, Seattle, Washington. Ritchie, J. T., D. C. Godwin and S. Otter-Nacke (1985). CERES Wheat: A Simulation Model of Wheat Growth and Development. College Station, Texas, Texas A&M University Press. sChulthess, U., K. Sdtroeder, A. Kamel, E. E. Hassanein, A. H. S. Shaban, A. A. EI-Ghani, A. A. EI-Shafy, J. Ritchie, R. Ward and J. Sticklen (1996). "NEPER-Weed: a picture-based expert system for weed identification." Agronomy ~3):
423-427.
Schulthess, U., K. Schroeder, A. Kamel, J. Sticklen, R. Ward. J. Ritchie, A. Rafea, A. Salah and A. S. Ali (1994). Weed Identification Using a PictureBased Hierarchical Classification System. AAAI Workshop on Artificial Intelligence in Agriculture and Natural Resource Management, Seattle, Washington, AAAI. Schulthess, U., R. Ward, K. Scroeder, A. Kame1, J. Sticklen, A. A. S. Ali and A,G. M. Abdel-Ghani (1994). Weed Identification Using a PictureBased Hierarchical Classification System . Decision Support-200I, Toronto, Canada. Sticklen, J., B. Chandrasekaran and J. Josephson (1987). "ModuIarity of Domain Knowledge." Expert Systems: Research and Applications 1.
10