A Proposal for Inductive Learning Agent Using First-Order Logic.
Tohgoroh Matsui, Nobuhiro Inuzuka, Hirohisa Seki · 2000
. In this paper, we propose new agent architecture which adapts its own behavior by avoiding actions which are predicted to be failure. We name this agent inductive learning agent (ILA). This agent consists of ve parts: Observer, Planner, Checker, Actor, and Learner. These parts use rst-order formalism and inductive logic programming (ILP) to acquire rules to predict. Adapting behavior of ILA works as follows: (1) Collect examples of actions. (2) Classify the examples. (3) Acquire prediction rules using ILP. (4) Behave under the prediction rules. We have implemented it in soccer using parts of the latest RoboCup competition champion CMUnited-99 and an ILP system Progol. We have conrmed that agents could acquire prediction rules and could adapt their behavior using the rules. 1 Introduction Researchers have studied on machine learning in the toy world and tried to bring them to the real world. However, it gradually became clear that some of these technologies developed i...