Aplib: An agent programming library for testing games
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Publication date
2020-01-01
Editors
An, Bo
El Fallah Seghrouchni, Amal
Sukthankar, Gita
Advisors
Supervisors
Document Type
Part of book
Metadata
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License
taverne
Abstract
Testing modern computer games is notoriously hard. Highly dynamic behavior, inherent non-determinism, and fine grained interactivity blow up their state space; too large for traditional automated testing techniques. An agent-based testing approach offers an alternative as agents' goal driven planning, adaptivity, and reasoning ability can provide an extra edge. This paper provides a summary of aplib, a Java library for programming intelligent test agents, featuring tactical programming as an abstract way to exert control on agents' underlying reasoning based behavior. Aplib is implemented in such a way to provide the fluency of a Domain Specific Language (DSL) while still staying in Java, and hence aplib programmers will keep all the advantages that Java programmers get: rich language features and a whole array of development tools.
Keywords
Agents tactical programming, AI for automated testing, Automated game testing, Intelligent agents for testing, Taverne, Artificial Intelligence, Software, Control and Systems Engineering
Citation
Prasetya, I S W B & Dastani, M 2020, Aplib : An agent programming library for testing games. in B An, A El Fallah Seghrouchni & G Sukthankar (eds), Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. vol. 2020-May, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), pp. 1972-1974, 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, Virtual, Auckland, New Zealand, 19/05/20. https://doi.org/10.5555/3398761.3399045, conference