CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Efficient reinforcement learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Knowledge Extraction from Transducer Neural Networks
Applied Intelligence
Towards Autonomous Robot Control via Self-Adapting Recurrent Networks
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots
Hybrid Neural Systems, revised papers from a workshop
Anchoring Symbols to Sensor Data: Preliminary Report
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Knowledge, action, and the frame problem
Artificial Intelligence
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
The origins of syntax in visually grounded robotic agents
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Evolutionary Development of Hierarchical Learning Structures
IEEE Transactions on Evolutionary Computation
Action and hierarchical levels of categories: A connectionist perspective
Cognitive Systems Research
A neuro-fuzzy network to generate human-understandable knowledge from data
Cognitive Systems Research
Mechanistic versus phenomenal embodiment: Can robot embodiment lead to strong AI?
Cognitive Systems Research
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Perception-action (PA) architectures are capable of solving a number of problems associated with artificial cognition, in particular, difficulties concerned with framing and symbol grounding. Existing PA algorithms tend to be 'horizontal' in the sense that learners maintain their prior percept-motor competences unchanged throughout learning. We here present a methodology for simultaneous 'horizontal' and 'vertical' perception-action learning in which there additionally exists the capability for incremental accumulation of novel percept-motor competences in a hierarchical fashion. The proposed learning mechanism commences with a set of primitive 'innate' capabilities and progressively modifies itself via recursive generalising of parametric spaces within the linked perceptual and motor domains so as to represent environmental affordances in maximally-compact manner. Efficient reparameterising of the percept domain is here accomplished by the exploratory elimination of dimensional redundancy and environmental context. Experimental results demonstrate that this approach exhibits an approximately linear increase in computational requirements when learning in a typical unconstrained environment, as compared with at least polynomially-increasing requirements for a classical perception-action system.