Action selection and task sequence learning for hybrid dynamical cognitive agents |
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Authors: | Eric Aaron Henny Admoni |
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Affiliation: | 1. Department of Immunology, School of Biological Sciences, Madurai Kamaraj University, Madurai - 625 021, Tamil Nadu, India;2. Department of Biotechnology & Genetic Engineering, School of Biotechnology, Bharathidasan University, Tiruchirappalli – 620 024, Tamil Nadu, India;3. Rajiv Gandhi Government General Hospital, Chennai-600 003, Tamil Nadu, India |
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Abstract: | 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. |
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