Machine Learning - Special issue on learning in autonomous robots
Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
Machine Learning - Special issue on learning in autonomous robots
Artificial Intelligence Review
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
Cooperative information sharing to improve distributed learning in multi-agent systems
Journal of Artificial Intelligence Research
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
Tractable POMDP representations for intelligent tutoring systems
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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Real robots with real sensors are not omniscient. When a robot's next course of action depends on information that is hidden from the sensors because of problems such as occlusion, restricted range, bounded field of view and limited attention, we say the robot suffers from the hidden state problem. State identification techniques use history information to uncover hidden state. Some previous approaches to encoding history include: finite state machines, recurrent neural networks and genetic programming with indexed memory. A chief disadvantage of all these techniques is their long training time. This paper presents instance-based state identification, a new approach to reinforcement learning with state identification that learns with much fewer training steps. Noting that learning with history and learning in continuous spaces both share the property that they begin without knowing the granularity of the state space, the approach applies instance-based (or “memory-based”) learning to history sequences-instead of recording instances in a continuous geometrical space, we record instances in action-percept-reward sequence space. The first implementation of this approach, called Nearest Sequence Memory, learns with an order of magnitude fewer steps than several previous approaches