Intention is choice with commitment
Artificial Intelligence
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
On Partially Observable MDPs and BDI Models
Selected papers from the UKMAS Workshop on Foundations and Applications of Multi-Agent Systems
Conflicts in teamwork: hybrids to the rescue
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Hierarchical multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems
On the relationship between MDPs and the BDI architecture
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
The multi-team formation precursor of teamwork
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Coordination with collective and individual decisions
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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In the aftermath of a large-scale disaster, agents' decisions derive from self-interested (e.g. survival), common-good (e.g. victims' rescue) and teamwork (e.g. fire extinction) motivations. However, current decision-theoretic models are either purely individual or purely collective and find it difficult to deal with motivational attitudes; on the other hand, mental-state based models find it difficult to deal with uncertainty. We propose a hybrid, CvI-JI, approach that combines: i) collective 'versus' individual (CvI) decisions, founded on the Markov decision process (MDP) quantitative evaluation of joint-actions, and ii) joint-intentions (JI) formulation of teamwork, founded on the belief-desire-intention (BDI) architecture of general mental-state based reasoning. The CvI-JI evaluation explores the performance's improvement during the process of learning a coordination policy in a partially observable stochastic domain.