Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Common LISP: the language (2nd ed.)
Common LISP: the language (2nd ed.)
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
Building agent teams using an explicit teamwork model and learning
Artificial Intelligence - Special issue on Robocop: the first step
Using simulation and critical points to define states in continuous search spaces
Proceedings of the 32nd conference on Winter simulation
An underlying model for defeat mechanisms
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
A Mixed-Initiative Planning Approach to Exploratory Data Analysis
A Mixed-Initiative Planning Approach to Exploratory Data Analysis
A hierarchical architecture for behavior-based robots
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Towards robust teams with many agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Agent modeling: models of defeat
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Proceedings of the 35th conference on Winter simulation: driving innovation
Knowledge Processing Middleware
SIMPAR '08 Proceedings of the 1st International Conference on Simulation, Modeling, and Programming for Autonomous Robots
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The Hierarchical Agent Control Architecture (HAC) is a general toolkit for specifying an agent's behavior. HAC supports action abstraction, resource management, sensor integration, and is well suited to controlling large numbers of agents in dynamic environments. It relies on three hierarchies: action, sensor, and context. The action hierarchy controls the agent's behavior. It is organized around tasks to be accomplished, not the agents themselves. This facilitates the integration of multi-agent actions and planning into the architecture. The sensor hierarchy provides a principled means for structuring the complexity of reading and transforming sensor information. Each level of the hierarchy integrates the data coming in from the environment into conceptual chunks appropriate for use by actions at this level. Actions and sensors are written using the same formalism. The context hierarchy is a hierarchy of goals. In addition to their primary goals, most actions are operating within a set of implicit assumptions. These assumptions are made explicit through the context hierarchy. We have developed a planner, GRASP, implemented within HAC, which is capable of resolving multiple goals in real time.HAC was intended to have wide applicability. It has been used to control agents in commercial computer games and physical robots. Our primary application domain is a simulator of land-based military engagements called “Capture the Flag.” HAC's simulation substrate models physics at an abstract level. HAC supports any domain in which behaviors can be reduced to a small set of primitive effectors such as {\sc move} and {\sc apply-force}. At this time defining agent behavior requires Lisp programming skills; we are moving towards more graphical programming languages.