A comparison of BDI based real-time reasoning and HTN based planning

Authors:
Lavindra de Silva;Lin Padgham
Affiliations:
School of Computer Science and Information Technology, RMIT University, Melbourne, Vic., Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, Vic., Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 4
Cited 5

On the complexity of blocks-world planning

Artificial Intelligence
A Planning Component for RETSINA Agents

ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Decision-making in an embedded reasoning system

IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
SHOP: simple hierarchical ordered planner

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2

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
Planning with time limits in BDI agent programming languages

CATS '07 Proceedings of the thirteenth Australasian symposium on Theory of computing - Volume 65
Motivation for a new formal framework for agent-oriented software engineering

International Journal of Agent-Oriented Software Engineering
Augmenting BDI agents with deliberative planning techniques

ProMAS'06 Proceedings of the 4th international conference on Programming multi-agent systems
A BDI agent programming language with failure handling, declarative goals, and planning

Autonomous Agents and Multi-Agent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Belief-Desire-Intention (BDI) model of agency is an architecture based on Bratman's theory of practical reasoning Hierarchical Task Network (HTN) decomposition on the other hand is a planning technique which has its roots in classical planning systems such as STRIPS Despite being used for different purposes, HTN and BDI systems appear to have a lot of similarities in the problem solving approaches they adopt This paper presents these similarities A systematic method for mapping between the two systems is developed, and experimental results for different kinds of environments are presented.