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
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Some experiments with a hybrid model for learning sequential decision making
Information Sciences—Informatics and Computer Science: An International Journal
Continuous categories for a mobile robot
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
| Hi-index | 0.00 |
High level conceptual thought seems to be at the basis of the impressive human cognitive ability. Classical top-down (Logic based) and bottom-up (Connectionist) approaches to the problem have had limited success to date. We identify a small body of work that represents a different approach to AI. We call this work the Bottom Up Symbolic (BUS) approach and present a new BUS method to concept construction. The main novelty of our work is that we apply statistical methods in the concept construction process. Our findings here suggest that such methods are necessary since a symbolic description of the true agent-environment interaction dynamics is often hidden among a background of non-representative descriptions, especially if data from unconstrained real-world experiments is considered. We consider such data (from a mobile robot randomly roaming an office environment) and show how our method can correctly grow a set of true concepts from the data.