SOAR: an architecture for general intelligence
Artificial Intelligence
Unified theories of cognition
Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
Modeling motivations and emotions as a basis for intelligent behavior
AGENTS '97 Proceedings of the first international conference on Autonomous agents
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
Cognitively inspired decision making for software agents: integrated mechanisms for action selection, expectation, automatization and non-routine problem solving
Using the representation in a neural network's hidden layer for task-specific focus of attention
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Model-based learning for mobile robot navigation from the dynamicalsystems perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems
Anticipatory Behavior in Adaptive Learning Systems
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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This paper describes the integration of several cognitively inspired anticipation and anticipatory learning mechanisms in an autonomous agent architecture, the Learning Intelligent Distribution Agent (LIDA) system. We provide computational mechanisms and experimental simulations for variants of payoff, state, and sensorial anticipatory mechanisms. The payoff anticipatory mechanism in LIDA is implicitly realized by the action selection dynamics of LIDA's decision making component, and is enhanced by importance and discrimination factors. A description of a non-routine problem solving algorithm is presented as a form of state anticipatory mechanism. A technique for action driven sensational and attentional biasing similar to a preafferent signal and preparatory attention is offered as a viable sensorial anticipatory mechanism. We also present an automatization mechanism coupled with an associated deautomatization procedure, and an instructionalist based procedural learning algorithm as forms of implicit and explicit anticipatory learning mechanisms.