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
Fast planning through planning graph analysis
Artificial Intelligence
The dMARS Architecture: A Specification of the Distributed Multi-Agent Reasoning System
Autonomous Agents and Multi-Agent Systems
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Hierarchical planning in BDI agent programming languages: a formal approach
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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)
Automated composition of web services by planning at the knowledge level
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Augmenting BDI agents with deliberative planning techniques
ProMAS'06 Proceedings of the 4th international conference on Programming multi-agent systems
Composing high-level plans for declarative agent programming
DALT'07 Proceedings of the 5th international conference on Declarative agent languages and technologies V
A BDI agent programming language with failure handling, declarative goals, and planning
Autonomous Agents and Multi-Agent Systems
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In order to facilitate the development of agent-based software, several agent programming languages and architectures, have been created. Plans in these architectures are often self-contained procedures with an associated triggering event and a context condition , while any further information about the consequences of executing a plan is absent. However, agents designed using such an approach have limited flexibility at runtime, and rely on the designer's ability to foresee all relevant situations an agent might have to handle. In order to overcome this limitation, we have created AgentSpeak(PL), an interpreter capable of performing state-space planning to generate new high-level plans. As the planning module creates new plans, the plan library is expanded, improving performance over time. However, for new plans to be useful in the long run, it is critical that the context conditions associated with new plans are carefully generated. In this paper we describe a plan reuse technique aimed at improving an agent's runtime performance by deriving optimal context conditions for new plans, allowing an agent to reuse generated plans as much as possible.