Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
O-Plan: the open planning architecture
Artificial Intelligence
A general programming language for unified planning and control
Artificial Intelligence - Special volume on planning and scheduling
Managing multiple tasks in complex, dynamic environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using simulation and critical points to define states in continuous search spaces
Proceedings of the 32nd conference on Winter simulation
Simulation using software agents II: domain-general simulation and planning with physical schemas
Proceedings of the 32nd conference on Winter simulation
Balancing between Reactivity and Deliberation in the ICAGENT Framework
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Bridging Deliberation and Reactivity in Cooperative Multi-Robot Systems through Map Focus
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
A simulation substrate for real-time planning TITLE2:
A simulation substrate for real-time planning TITLE2:
A mixed-initiative planning approach to exploratory data analysis
A mixed-initiative planning approach to exploratory data analysis
Decision-making in an embedded reasoning system
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Bridging Deliberation and Reactivity in Cooperative Multi-Robot Systems through Map Focus
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
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The Hierarchical Agent Control Architecture (HAC) is a general toolkit for specifying an agent's behavior. By organizing the hierarchy around tasks to be accomplished, not the agents themselves, it is easy to incorporate multi-agent actions and planning into the architecture. In addition, HAC supports action abstraction, resource management, sensor integration, and is well suited to controlling large numbers of agents in dynamic environments. Unlike other agent architectures, HAC does not conceptually distinguish reactive from deliberative, or single-agent from multi-agent behaviors. There is no pre-determined number of cognitive "levels" in the hierarchy|all actions share the same form and are implemented with the same functions. GRASP is a multi-goal partial hierarchical planner that has been implemented using the HAC framework. GRASP illustrates two points: Firstly, that the same HAC mechanisms used to write reactive actions can be used to implement a cognitive activity such as planning; and secondly, that the problem of integrating reactive and deliberative behavior itself can be viewed as having to simultaneously achieve multiple goals. Throughout the paper, we show how HAC and GRASP were applied to an adversarial, real-time domain based on the game of "Capture the Flag".