Object-oriented modeling and design
Object-oriented modeling and design
Object-oriented analysis and design with applications (2nd ed.)
Object-oriented analysis and design with applications (2nd ed.)
JAM: a BDI-theoretic mobile agent architecture
Proceedings of the third annual conference on Autonomous Agents
Top-down search for coordinating the hierarchical plans of multiple agents
Proceedings of the third annual conference on Autonomous Agents
Conflict representation and classification in a domain-independent conflict management framework
Proceedings of the third annual conference on Autonomous Agents
Theory for coordinating concurrent hierarchical planning agents using summary information
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Evaluating new options in the context of existing plans
Artificial Intelligence
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
Agent Programming with Declarative Goals
ATAL '00 Proceedings of the 7th International Workshop on Intelligent Agents VII. Agent Theories Architectures and Languages
Transaction Oriented Computational Models for Multi-Agent Systems
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
Discovering and exploiting synergy between hierarchical planning agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Agent programming in dribble: from beliefs to goals using plans
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Detecting & exploiting positive goal interaction in intelligent agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Continuous refinement of agent resource estimates
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
On proactivity and maintenance goals
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Theoretical and experimental results on the goal-plan tree problem
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Detecting & avoiding interference between goals in intelligent agents
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An integrated formal framework for reasoning about goal interactions
DALT'11 Proceedings of the 9th international conference on Declarative Agent Languages and Technologies
Reasoning about preferences in intelligent agent systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Declarative planning in procedural agent architectures
Expert Systems with Applications: An International Journal
An operational semantics for the goal life-cycle in BDI agents
Autonomous Agents and Multi-Agent Systems
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It is important that intelligent agents are able to pursue multiple goals in parallel, in a rational manner. In this work we have described the careful empirical evaluation of the value of data structures and algorithms developed for reasoning about both positive and negative goal interactions. These mechanisms are incorporated into a commercial agent platform and then evaluated in comparison to the platform without these additions. We describe the data structures and algorithms developed, and the X-JACK system, which incorporates these into JACK, a state of the art agent development toolkit. There are three basic kinds of reasoning that are developed: reasoning about resource conflicts, reasoning to avoid negative interactions that can happen when steps of parallel goals are arbitrarily interleaved, and reasoning to take advantage of situations where a single step can help to achieve multiple goals. X-JACK is experimentally compared to JACK, under a range of situations designed to stress test the reasoning algorithms, as well as situations designed to be more similar to real applications. We found that the cost of the additional reasoning is small, even with large numbers of goal interactions to reason about. The benefit however is noticeable, and is statistically significant, even when the amount of goal interactions is relatively small.