AgentSpeak(L): BDI agents speak out in a logical computable language
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Modeling rational agents with a BDI-architecture
Readings in agents
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology)
Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology)
Intentional learning agent architecture
Autonomous Agents and Multi-Agent Systems
CTL AgentSpeak(L): A specification language for agent programs
Journal of Algorithms
Commitment and effectiveness of situated agents
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Top-down induction of first-order logical decision trees
Artificial Intelligence
Inductive logic programming algorithm for estimating quality of partial plans
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Decision as choice of potential intentions
Web Intelligence and Agent Systems
Hi-index | 0.00 |
This paper presents JILDT (Jason Induction of Logical Decision Trees), a library that defines two learning agent classes for Jason, the well known java-based implementation of AgentSpeak(L). Agents defined as instances of JILDT can learn about their reasons to adopt intentions performing first-order induction of decision trees. A set of plans and actions are defined in the library for collecting training examples of executed intentions, labeling them as succeeded or failed executions, computing the target language for the induction, and using the induced trees to modify accordingly the plans of the learning agents. The library is tested studying commitment: A simple problem in a world of blocks is used to compare the behavior of a default Jason agent that does not reconsider his intentions, unless they fail; a learning agent that reconsiders when to adopt intentions by experience; and a single-minded agent that also drops intentions when this is rational. Results are very promissory for both, justifying a formal theory of single-mind commitment based on learning, as well as enhancing the adopted inductive process.