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Genetic encoding of agent behavioral strategy (1998)
| Content Provider | CiteSeerX |
|---|---|
| Author | Calderoni, Stéphane Marcenac, Pierre Courdier, Rémy |
| Description | The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and is weighted by an adaptation function as a quality factor that reflects its relevance as good solution for the learning task. It is computed using heterogeneous reinforcement techniques associating immediate reinforcements and delayed reinforcements as dynamic progress estimators. 1. |
| File Format | |
| Language | English |
| Publisher Date | 1998-01-01 |
| Publisher Institution | In Proceedings of International Conference on Multi Agent Systems |
| Access Restriction | Open |
| Subject Keyword | Delayed Reinforcement General Framework Adaptation Function Artificial Evolution Autonomous Agent Behavioral Strategy Genetic Evolving Process Genetic Programming Intelligent Collective Behavior Agent Behavioral Strategy Genetic Encoding Dynamic Progress Estimator Automatic Design Process Reinforcement Learning Tree-based Structure Automatic Generation Behavior-based System High Level Behavior Good Solution Learning Task Quality Factor Heterogeneous Reinforcement Technique Immediate Reinforcement |
| Content Type | Text |
| Resource Type | Article |
