Model-Based Reflection for Agent Evolution

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/6597

Title: Model-Based Reflection for Agent Evolution
Author: Murdock, J. William
Abstract: Adaptability is a key characteristic of intelligence. My research explores techniques for enabling software agents to adapt themselves as their functional requirements change incrementally. In the domain of manufacturing, for example, a software agent designed to assemble physical artifacts may be given a new goal of disassembling artifacts. As another example, in the internet domain, a software agent designed to browse some types of documents may be called upon to browse a document of another type. In particular, my research examines the use of reflection (an agent's knowledge and reasoning about itself) to accomplish evolution (incremental adaptation of an agent's capabilities). I have developed a language called TMKL (Task-Method-Knowledge Language) that enables modeling of an agent's composition and functioning. A TMKL model of an agent explicitly represents the tasks the agent addresses, the methods it applies, and the knowledge it uses. TMKL models are hierarchical, i.e., they represents tasks, methods and knowledge at multiple levels of abstraction. I have also developed a reasoning shell called REM (Reflective Evolutionary Mind) which provides support for the execution and evolution of agents represented in TMKL. REM employs a variety of strategies for evolving TMKL agents. Some of these strategies are purely model-based: knowledge of composition and functioning encoded in TMKL directly enables adaptation. REM also employs two traditional artificial intelligence and machine learning techniques: generative planning and reinforcement learning. The combination of model-based adaptation, generative planning, and reinforcement learning constitutes a mechanism for re ective agent evolution which is capable of addressing a variety of problems to which none of these individual approaches alone is suited. My research demonstrates the computational feasibility of this mechanism using experiments involving a variety of intelligent software agents in a variety of domains.
Type: Technical Report
URI: http://hdl.handle.net/1853/6597
Date: 2000
Relation: CC Technical Report; GIT-CC-00-34
Publisher: Georgia Institute of Technology
Subject: Software agents
Adaptation
Abstractions
Machine learning
Artificial intelligence
Reinforcement learning
Generative planning
Reflective agent evolution

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