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STAIRS 2006:  Proceedings of the Third Starting AI Researchers' Symposium, Volume 142 Frontiers in Artificial Intelligence and Applications

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The field of Artificial Cognition has arisen recently with the intention of developing agents capable to perceive, explore, learn and perform reasoning about the external world. A number of machine learning approaches have been adopted in order to make agent behaviour cognitive. However, most existing systems rely on an external expert guiding the process of learning, either explicitly defining correct examples (supervised methods) or providing rewards (reinforcement learning) [1,2]. Fully autonomous world exploration and self-driven learning have not yet been achieved.

Nevertheless, some successful progress towards developing cognitive agents can be found in the approaches of Open-Ended Architecture, Simultaneous Localization and Mapping (SLAM Robotics) and Spatial-Semantic Hierarchy.

Most of the open-ended architectures employ Hierarchical Perception-Action Approach, first introduced by Brooks [3], followed by OPAL architecture [4] and Low-Level Perception-Action Linking [5]. This direction is primarily intended to mimic connection between perception and action found in living organisms. There also has been suggested a subsumption principle which is based on hierarchical control of behaviour wherein abstract symbolic models emerge by bottom-up inference and the higher levels adjust the lower via inhibition.
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