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 (A 2BL), 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 A 2BL can encode the same agent much more naturally. Copyright © 2008 ACM.
CITATION STYLE
Simpkins, C., Bhat, S., Isbell, C., & Mateas, M. (2008). Towards adaptive programming integrating reinforcement learning into a programming language. In Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA (pp. 603–613). https://doi.org/10.1145/1449764.1449811
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