PC AI
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
State abstraction for programmable reinforcement learning agents
Eighteenth national conference on Artificial intelligence
On the difficulty of modular reinforcement learning for real-world partial programming
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multiple-goal reinforcement learning with modular Sarsa(O)
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Adaptation-based programming in java
Proceedings of the 20th ACM SIGPLAN workshop on Partial evaluation and program manipulation
Towards programming languages for machine learning and data mining
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
The actor's view of automated planning and acting: A position paper
Artificial Intelligence
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Current programming languages and software engineering paradigms are proving insufficient for building intelligent multi-agent systems--such as interactive games and narratives--where developers are called upon to write increasingly complex behavior for agents in dynamic environments. A promising solution is to build adaptive systems; that is, to develop software written specifically to adapt to its environment by changing its behavior in response to what it observes in the world. In this paper we describe a new programming language, An Adaptive Behavior Language (A2BL), that implements adaptive programming primitives to support partial programming, a paradigm in which a programmer need only specify the details of behavior known at code-writing time, leaving the run-time system to learn the rest. Partial programming enables programmers to more easily encode software agents that are difficult to write in existing languages that do not offer language-level support for adaptivity. We motivate the use of partial programming with an example agent coded in a cutting-edge, but non-adaptive agent programming language (ABL), and show how A2BL can encode the same agent much more naturally.