Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reasoning about Intentions in Uncertain Domains
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Hybrid System Models of Navigation Strategies for Games and Animations
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
A Hybrid Dynamical Systems Approach to Intelligent Low-Level Navigation
CA '02 Proceedings of the Computer Animation
Multiphase Learning for an Interval-Based Hybrid Dynamical System
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Planning with iFALCON: Towards A Neural-Network-Based BDI Agent Architecture
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Deliberation Process in a BDI Model with Bayesian Networks
Agent Computing and Multi-Agent Systems
Hybrid BDI-POMDP framework for multiagent teaming
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The dynamics of action selection
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Reactive reasoning and planning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
IEEE Transactions on Evolutionary Computation
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As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief-desire-intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods-such as Hebbian strengthening of links between co-active elements, and belief-intention learning of task-specific relationships-modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.