Difference between revisions of "Learning"
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| − | = Adaptive Behavioral Programming = | + | = Adaptive Behavioral Programming = |
| + | |||
| + | We introduce a way to program adaptive reactive | ||
| + | systems, using behavioral, scenario-based programming. | ||
| + | Extending the semantics of BPJ with reinforcements | ||
| + | allows the programmer not only to specify what | ||
| + | the system should do or must not do, but also what it should | ||
| + | try to do, in an intuitive and incremental way. By integrating | ||
| + | behavioral programs with reinforcement learning methods, | ||
| + | the program can adapt to the environment, and try to achieve | ||
| + | the desired goals. | ||
== Reference Materials == | == Reference Materials == | ||
| − | * Paper: [http://www.wisdom.weizmann.ac.il/~harel/papers/Adaptive%20BP.pdf Adaptive Behavioral Programming] | + | * Paper: [http://www.wisdom.weizmann.ac.il/~harel/papers/Adaptive%20BP.pdf Adaptive Behavioral Programming] |
* See [http://www.wisdom.weizmann.ac.il/~bprogram/code/ReinforcementLearningBPJ.zip reference materials ] | * See [http://www.wisdom.weizmann.ac.il/~bprogram/code/ReinforcementLearningBPJ.zip reference materials ] | ||
== Contents of reference material == | == Contents of reference material == | ||
| − | * A base version of BPJ | + | * A base version of BPJ |
* A Java library with extensions to BPJ for reinforcement learning. Import and add to the classpath | * A Java library with extensions to BPJ for reinforcement learning. Import and add to the classpath | ||
* Example: A Salad cutting robot. The b-threads provide an underspecification for a simulated robot that needs to pick up vegetables, cut them and serve them to customers. Using reinforcement learning, the robot learns the correct o in which to execute these actions. | * Example: A Salad cutting robot. The b-threads provide an underspecification for a simulated robot that needs to pick up vegetables, cut them and serve them to customers. Using reinforcement learning, the robot learns the correct o in which to execute these actions. | ||
* Example: Tic Tac Toe, where the application learns how to play despite underspecification. | * Example: Tic Tac Toe, where the application learns how to play despite underspecification. | ||
Revision as of 18:50, 27 April 2014
Adaptive Behavioral Programming
We introduce a way to program adaptive reactive systems, using behavioral, scenario-based programming. Extending the semantics of BPJ with reinforcements allows the programmer not only to specify what the system should do or must not do, but also what it should try to do, in an intuitive and incremental way. By integrating behavioral programs with reinforcement learning methods, the program can adapt to the environment, and try to achieve the desired goals.
Reference Materials
Contents of reference material
- A base version of BPJ
- A Java library with extensions to BPJ for reinforcement learning. Import and add to the classpath
- Example: A Salad cutting robot. The b-threads provide an underspecification for a simulated robot that needs to pick up vegetables, cut them and serve them to customers. Using reinforcement learning, the robot learns the correct o in which to execute these actions.
- Example: Tic Tac Toe, where the application learns how to play despite underspecification.
