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
Multivariate Clustering by Dynamics
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Algorithm for Segmenting Categorical Time Series into Meaningful Episodes
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Fluent Learning: Elucidating the Structure of Episodes
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Grounding knowledge in sensors: unsupervised learning for language and planning
Grounding knowledge in sensors: unsupervised learning for language and planning
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Reference frames for animate vision
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Schematic aspect for autonomous agent
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
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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.