Toward a paradigm for simulating intelligent agents
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| Title: | Toward a paradigm for simulating intelligent agents |
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| Authors: | Castillo, David |
| Committee Members: | Swart, William W. |
| Summary: | Current simulation and modeling techniques provide little support for modeling and simulating the intellectual properties of intelligent agents. Recent research efforts in knowledge based simulation, favor conventional rule based expert systems for representing the cognitive thought process of an intelligent agent. The problem with this approach, is that it does not effectively address the issues surrounding anticipation, experiential learning, failure avoidance, goal attainment, and adaptive behavior. Rather, rule based approaches for simulating intelligent agent activity represent behavior as the product of chaining together collections of predefined rules. Each time the agent is faced with performing a behavior, a rule base is searched for an appropriate rule describing a specific behavior. Once the rule is located, it is blindly executed without concern for understanding the nature of the behavior. Furthermore, the agent gains nothing from the experience of processing the rule. As a result, intelligent agents lack the fundamental properties and features that make them intelligent. The approach taken by this research vie'WS intelligent agents as possessing the cognitive capability to reason about their processing domain, and dynamically adapt their behavior to a changing environment. Intelligent agents do not blindly follow a sequence for predefined instructions. Rather, they begin with an initial plan, and adapt that plan to achieve their goals. More importantly, intelligent agents learn from experience, in that the results of their behavior are stored in long term memory where they are used again in future situations. This philosophy, referred to as "Case Based" reasoning by Artificial Intelligence researchers, provides the basis for knowledge retrieval, organization, and learning. Achieving this form of intelligent agent requires a representation system specifically designed to provide explicit support for goal directed reasoning, anticipation, dynamic memory, plan organization and retrieval, and learning. Since planning is a fundamental aspect of intelligent agent behavior, both declarative and procedural knowledge must also be supported. This research examines the intelligent agent and proposes an Intelligent Agent Cognitive System for supporting the cognitive aspects of intelligent agent activity. |
| URL: | https://stars.library.ucf.edu/rtd/3808 |
| Database: | OpenDissertations |
| Abstract: | Current simulation and modeling techniques provide little support for modeling and simulating the intellectual properties of intelligent agents. Recent research efforts in knowledge based simulation, favor conventional rule based expert systems for representing the cognitive thought process of an intelligent agent. The problem with this approach, is that it does not effectively address the issues surrounding anticipation, experiential learning, failure avoidance, goal attainment, and adaptive behavior. Rather, rule based approaches for simulating intelligent agent activity represent behavior as the product of chaining together collections of predefined rules. Each time the agent is faced with performing a behavior, a rule base is searched for an appropriate rule describing a specific behavior. Once the rule is located, it is blindly executed without concern for understanding the nature of the behavior. Furthermore, the agent gains nothing from the experience of processing the rule. As a result, intelligent agents lack the fundamental properties and features that make them intelligent. The approach taken by this research vie'WS intelligent agents as possessing the cognitive capability to reason about their processing domain, and dynamically adapt their behavior to a changing environment. Intelligent agents do not blindly follow a sequence for predefined instructions. Rather, they begin with an initial plan, and adapt that plan to achieve their goals. More importantly, intelligent agents learn from experience, in that the results of their behavior are stored in long term memory where they are used again in future situations. This philosophy, referred to as "Case Based" reasoning by Artificial Intelligence researchers, provides the basis for knowledge retrieval, organization, and learning. Achieving this form of intelligent agent requires a representation system specifically designed to provide explicit support for goal directed reasoning, anticipation, dynamic memory, plan organization and retrieval, and learning. Since planning is a fundamental aspect of intelligent agent behavior, both declarative and procedural knowledge must also be supported. This research examines the intelligent agent and proposes an Intelligent Agent Cognitive System for supporting the cognitive aspects of intelligent agent activity. |
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