Learning to improve agent behaviours in GOAL

Dhirendra Singh, Koen V. Hindriks

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


This paper investigates the issue of adaptability of behaviour in the context of agent-oriented programming. We focus on improving action selection in rule-based agent programming languages using a reinforcement learning mechanism under the hood. The novelty is that learning utilises the existing mental state representation of the agent, which means that (i) the programming model is unchanged and using learning within the program becomes straightforward, and (ii) adaptive behaviours can be combined with regular behaviours in a modular way. Overall, the key to effective programming in this setting is to balance between constraining behaviour using operational knowledge, and leaving flexibility to allow for ongoing adaptation. We illustrate this using different types of programs for solving the Blocks World problem.

Original languageEnglish
Title of host publicationProgramming Multi-Agent Systems - 10th International Workshop, ProMAS 2012, Revised Selected Papers
Number of pages16
Publication statusPublished - 5 Sept 2013
Externally publishedYes
Event10th International Workshop on Programming Multi-Agent Systems, ProMAS 2012 - Valencia, Spain
Duration: 5 Jun 20125 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7837 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Workshop on Programming Multi-Agent Systems, ProMAS 2012


  • Agent programming
  • reinforcement learning
  • rule selection


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