Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Reasoning about knowledge
A computational theory of grounding in natural language conversation
A computational theory of grounding in natural language conversation
Collaborative plans for complex group action
Artificial Intelligence
Sound and efficient closed-world reasoning for planning
Artificial Intelligence
Intelligent planning: a decomposition and abstraction based approach
Intelligent planning: a decomposition and abstraction based approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Extending Graphplan to handle uncertainty and sensing actions
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Top-down search for coordinating the hierarchical plans of multiple agents
Proceedings of the third annual conference on Autonomous Agents
Distributed Algorithms
Functional strips: a more flexible language for planning and problem solving
Logic-based artificial intelligence
Formal Theories of the Commonsense World
Formal Theories of the Commonsense World
COLLAGEN: A Collaboration Manager for Software Interface Agents
User Modeling and User-Adapted Interaction
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
Continual coordination through shared activities
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A collaborative planning model of intentional structure
Computational Linguistics
An efficient algorithm for multiagent plan coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Partial-order planning with concurrent interacting actions
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
The fast downward planning system
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
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning executing sensing and replanning for information gathering
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Dynamic plot generation by continual multiagent planning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Task planning for an autonomous service robot
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
A survey of motivation frameworks for intelligent systems
Artificial Intelligence
Planning for the environmental quality of urba microclimate: a multiagent-based approach
CDVE'11 Proceedings of the 8th international conference on Cooperative design, visualization, and engineering
Distributed decision support system for airport ground handling management using WSN and MAS
Engineering Applications of Artificial Intelligence
Online planning for ad hoc autonomous agent teams
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Planning under partial observability by classical replanning: theory and experiments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Abductive reasoning for continual dialogue understanding
ESSLLI'10 Proceedings of the 2010 international conference on New Directions in Logic, Language and Computation
Robot george: interactive continuous learning of visual concepts
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Applying hybrid learning approach to RoboCup's strategy
Journal of Systems and Software
Fault-tolerant planning under uncertainty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
The actor's view of automated planning and acting: A position paper
Artificial Intelligence
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In order to behave intelligently, artificial agents must be able to deliberatively plan their future actions. Unfortunately, realistic agent environments are usually highly dynamic and only partially observable, which makes planning computationally hard. For most practical purposes this rules out planning techniques that account for all possible contingencies in the planning process. However, many agent environments permit an alternative approach, namely continual planning, i.e. the interleaving of planning with acting and sensing. This paper presents a new principled approach to continual planning that describes why and when an agent should switch between planning and acting. The resulting continual planning algorithm enables agents to deliberately postpone parts of their planning process and instead actively gather missing information that is relevant for the later refinement of the plan. To this end, the algorithm explictly reasons about the knowledge (or lack thereof) of an agent and its sensory capabilities. These concepts are modelled in the planning language (MAPL). Since in many environments the major reason for dynamism is the behaviour of other agents, MAPL can also model multiagent environments, common knowledge among agents, and communicative actions between them. For Continual Planning, MAPL introduces the concept of of assertions, abstract actions that substitute yet unformed subplans. To evaluate our continual planning approach empirically we have developed MAPSIM, a simulation environment that automatically builds multiagent simulations from formal MAPL domains. Thus, agents can not only plan, but also execute their plans, perceive their environment, and interact with each other. Our experiments show that, using continual planning techniques, deliberate action planning can be used efficiently even in complex multiagent environments.