Aplib: Tactical Agents for Testing Computer Games

Publication date

2020

Authors

Prasetya, WishnuISNI 0000000396460003
Dastani, MehdiISNI 0000000043464658
Prada, Rui
Vos, Tanja EJ
Dignum, FrankISNI 0000000121013677
Kifetew, Fitsum

Editors

Baroglio, C.
Hubner, J.F.
Winikoff, M.

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

taverne

Abstract

Modern interactive software, such as computer games, employ complex user interfaces. Although these user interfaces make the games attractive and powerful, unfortunately they also make them extremely difficult to test. Not only do we have to deal with their functional complexity, but also the fine grained interactivity of their user interface blows up their interaction space, so that traditional automated testing techniques have trouble handling it. An agent-based testing approach offers an alternative solution: agents’ goal driven planning, adaptivity, and reasoning ability can provide an extra edge towards effective navigation in complex interaction space. This paper presents aplib, a Java library for programming intelligent test agents, featuring novel tactical programming as an abstract way to exert control over agents’ underlying reasoning-based behavior. This type of control is suitable for programming testing tasks. Aplib is implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Its embedded DSL approach also means that aplib programmers will get all the advantages that Java programmers get: rich language features and a whole array of development tools.

Keywords

automated game testing, AI for automated testing, agents tactical programming, intelligent agentsfor testing, intelligent agent programming, Taverne

Citation

Prasetya, I S W B, Dastani, M, Prada, R, Vos, T EJ, Dignum, F & Kifetew, F 2020, Aplib: Tactical Agents for Testing Computer Games. in C Baroglio, J F Hubner & M Winikoff (eds), Engineering Multi-Agent Systems : 8th International Workshop , EMAS 2020, Auckland, New Zealand, May 8–9, 2020, Revised Selected Papers. vol. 12589, Lecture Notes in Artificial Intelligence, Springer, pp. 21-41. https://doi.org/10.1007/978-3-030-66534-0