Collaborative plans for complex group action
Artificial Intelligence
Intelligent planning: a decomposition and abstraction based approach
Intelligent planning: a decomposition and abstraction based approach
Top-down search for coordinating the hierarchical plans of multiple agents
Proceedings of the third annual conference on Autonomous Agents
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
Partial-order planning with concurrent interacting actions
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning in nondeterministic domains under partial observability via symbolic model checking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Continual collaborative planning for mixed-initiative action and interaction
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Color learning and illumination invariance on mobile robots: A survey
Robotics and Autonomous Systems
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Emotional range in value-sensitive deliberation
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In highly dynamic environments, e.g. multiagent systems, finding optimal action plans is practically impossible since individual agents lack important knowledge at planning time or this knowledge has become obsolete when a plan is executed. It is often more practical in such environments to enable agents to actively extend their knowledge as part of their plans and then revise their decisions in light of these update. In this paper, we describe a new principled approach to Continual Planning, i.e. the integration of Planning, Execution and Monitoring. The algorithm deliberately postpones parts of the planning process to later stages in an agent's plan-act-monitor cycle and automatically determines when to switch back to refining or revising a partly executed plan. To evaluate our (and others') Continual Planning techniques we have developed a simulation environment where formal MA Planning domains are not only used by planning agents but also as the basis of the simulation model such that agents can not only plan, but execute actions and perceive their environment. Our experiments show that, using continual planning techniques, deliberate action planning can be used efficiently even in complex multiagent environments.