Meta-case-based reasoning: self-improvement through self-understanding
- 15 February 2008
- journal article
- research article
- Published by Taylor & Francis Ltd in Journal of Experimental & Theoretical Artificial Intelligence
- Vol. 20 (1), 1-36
- https://doi.org/10.1080/09528130701472416
Abstract
The ability to adapt is a key characteristic of intelligence. In this work we investigate model-based reasoning for enabling intelligent software agents to adapt themselves as their functional requirements change incrementally. We examine the use of reflection (an agent's knowledge and reasoning about itself) to accomplish adaptation (incremental revision of an agent's capabilities). Reflection in this work is enabled by a language called TMKL (Task-Method-Knowledge Language) which supports modelling of an agent's composition and teleology. A TMKL model of an agent explicitly represents the tasks the agent addresses, the methods it applies, and the knowledge it uses. These models are used in a reasoning shell called REM (Reflective Evolutionary Mind). REM enables the execution and incremental adaptation of agents that contain TMKL models of themselves.Keywords
This publication has 20 references indexed in Scilit:
- Adaptation-guided retrieval: questioning the similarity assumption in reasoningArtificial Intelligence, 1998
- Fast planning through planning graph analysisArtificial Intelligence, 1997
- Meta-cases: Explaining case-based reasoningLecture Notes in Computer Science, 1996
- Plan reuse versus plan generation: a theoretical and empirical analysisArtificial Intelligence, 1995
- An architecture for adaptive intelligent systemsArtificial Intelligence, 1995
- Integrating planning and learning: the PRODIGY architectureJournal of Experimental & Theoretical Artificial Intelligence, 1995
- FUNCTIONAL REPRESENTATION AND REASONING FOR REFLECTIVE SYSTEMSApplied Artificial Intelligence, 1995
- Automatically generating abstractions for planningArtificial Intelligence, 1994
- Planning and meta-planning (MOLGEN: Part 2)Artificial Intelligence, 1981
- Planning in a hierarchy of abstraction spacesArtificial Intelligence, 1974