Introduction to algorithms
Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
Acquiring domain knowledge for planning by experimentation
Acquiring domain knowledge for planning by experimentation
Learning by observation and practice: a framework for automatic acquisition of planning operators
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Map learning with uninterpreted sensors and effectors
Map learning with uninterpreted sensors and effectors
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward natural language interfaces for robotic agents: grounding linguistic meaning in sensors
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Review: learning like a baby: A survey of artificial intelligence approaches
The Knowledge Engineering Review
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A robotic agent experiences a world of continuous multivariate sensations and chooses its actions from continuous action spaces. Unless the agent is able to successfully partition these into functionally similar classes, its ability to interact with the world will be extremely limited. We present a method whereby an unsupervised robotic agent learns to discriminate discrete actions out of its continuous action parameters. These actions are discriminated because they lead to qualitatively distinct outcomes in the robot's sensor space. Once found, these actions can be used by the robot as primitives for further exploration of its world. We present results gathered using a Pioneer 1 mobile robot.