Cyc: toward programs with common sense
Communications of the ACM
Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Neo: learning conceptual knowledge by sensorimotor interaction with an environment
AGENTS '97 Proceedings of the first international conference on Autonomous agents
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
Identifying qualitatively different outcomes of actions: gaining autonomy through learning
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
The spatial semantic hierarchy
Artificial Intelligence
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Grounding knowledge in sensors: unsupervised learning for language and planning
Grounding knowledge in sensors: unsupervised learning for language and planning
Semiotic schemas: a framework for grounding language in action and perception
Artificial Intelligence - Special volume on connecting language to the world
What can i do with this?: finding possible interactions between characters and objects
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Essential Phenomena of General Intelligence
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
On the integration of grounding language and learning objects
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Societal grounding is essential to meaningful language use
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning to interpret utterances using dialogue history
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
The marchitecture: a cognitive architecture for a robot baby
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Autonomous Agents and Multi-Agent Systems
Semiotic schemas: A framework for grounding language in action and perception
Artificial Intelligence - Special volume on connecting language to the world
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In this paper we claim that meaningful representations can be learned by programs, although today they are almost always designed by skilled engineers. We discuss several kinds of meaning that representations might have, and focus on a functional notion of meaning as appropriate for programs to learn. Specifically, a representation is meaningful if it incorporates an indicator of external conditions and if the indicator relation informs action. We survey methods for inducing kinds of representations we call structural abstractions. Prototypes of sensory time series are one kind of structural abstraction, and though they are not denoting or compositional, they do support planning. Deictic representations of objects and prototype representations of words enable a program to learn the denotational meanings of words. Finally, we discuss two algorithms designed to find the macroscopic structure of episodes in a domain-independent way.