Integrating Reinforcement Learning into a Programming Language
2010
Creating artificial intelligent agents that are high-fidelity simulations of natural agents will require the engagement of behavioral scientists. However, agent programming systems that are accessible to behavioral scientists are too limited to create rich agents, and systems for creating rich agents are accessible mainly to computer scientists, not behavioral scientists. We are solving this problem by engaging behavioral scientists in the design of a programming language, and integrating reinforcement learning into the programming language. This strategy will help our language achieve adaptivity, modularity, and, most importantly, accessibility to behavioral scientists. In addition to allowing behavioral scientist to write rich agent programs, our language — AFABL (A Friendly Behavior Language) — will enable a true discipline of modular agent software engineering with broad implications for games, interactive storytelling, and social simulations.
Keywords:
- Fourth-generation programming language
- First-generation programming language
- Machine learning
- Artificial intelligence
- Very high-level programming language
- Programming domain
- Programming language implementation
- Programming language
- Computer science
- Fifth-generation programming language
- Extensible programming
- Programming language theory
- High-level programming language
- Natural language programming
- Inductive programming
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