Title :
An Approach of Temporal Difference Learning Using Agent-Oriented Programming
Author :
Badica, Amelia ; Badica, Costin ; Ivanovic, Mirjana ; Mitrovic, Dejan
Author_Institution :
Univ. of Craiova, Craiova, Romania
Abstract :
Reinforcement Learning -- RL is an important agent problem that was not approached using the tools provided by Agent Oriented Programming -- AOP. Agent Speak (L) and its Jason implementation based on Java platform are representing state-of-the-art approaches of AOP based on the Belief-Desire-Intention -- BDI model. Temporal Difference Learning -- TDL is a passive RL method that can be used by an agent to learn its utility function while it is acting according to a given policy in an uncertain and dynamic environment. In this paper we present an approach for modeling and implementation of TDL using the Jason AOP language. So, our paper is presenting a contribution towards narrowing the gap between RL and AOP, by endowing BDI agents with TDL skills.
Keywords :
Java; belief maintenance; learning (artificial intelligence); object-oriented programming; software agents; Agent Speak; BDI agents; BDI model; Jason AOP language; Jason implementation; Java platform; TDL; agent learning; agent problem; agent-oriented programming; belief-desire-intention model; dynamic environment; passive RL method; reinforcement learning; temporal difference learning; uncertain environment; utility function; Cognition; Electronic mail; History; Java; Mathematical model; Programming; agent-oriented programming; reinforcement learning;
Conference_Titel :
Control Systems and Computer Science (CSCS), 2015 20th International Conference on
Conference_Location :
Bucharest
Print_ISBN :
978-1-4799-1779-2
DOI :
10.1109/CSCS.2015.71