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 al.l the advantages that Java programmers get: rich language features and a whole array of development tools .
This work is supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 856716 Project iv4XR (Intelligent Verification/Validation for Extended Reality Based Systems).
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Notes
- 1.
“Agent Programming Library”, https://iv4xr-project.github.io/aplib/.
- 2.
- 3.
Note that \(\mathbf{action}\), \(\mathbf{do}_2\), and \(\mathbf{on}\_\) are not Java keywords. They are just methods. However, they also implement the Fluent Interface design pattern [19] commonly used in embedded Domain Specific Languages (DSLs) to ‘trick’ the syntax restriction of the host language to allow methods to be called in a sequence as if they form a sentence to improve the DSL’s fluency.
- 4.
This scheme of using r essentially simulates unification a la pgrules in 2APL. Unification plays an important role in 2APL. The action in (4) corresponds to pgrule \(q(r)? \; | \; f(r)\) The parameter s (the agent’s state/belief) is kept implicit in pgrules. In 2APL this action is executed through Prolog, where q is a Prolog query and r is obtained through unification with the fact base representing the agent’s state.
- 5.
While it is true that we can encode all control in action guards, this would not be an abstract way of programming tactical control and would ultimately result in error prone code.
- 6.
Earlier, in Sect. 1, we mentioned a relation with theorem provers. LCF-family theorem provers like HOL and Isabelle also have a concept of ’tactic’, which basically is a function that constructs a proof of a given conjecture [12, 22, 40]. Since the solving proof is usually not known upfront, similar tactic combinators are used to control a search over the possible proof space. E.g. in HOL we have \(\mathsf{THEN}\), and \(\mathsf{ORLSE}\). These correspond to our \(\mathsf{SEQ}\) and \(\mathsf{FIRSTof}\). HOL’s \(\mathsf{REPEAT}\) has no direct tactical counterpart in aplib, though aplib’s deliberation cycles implicitly introduce a top-level repetition —this will be elaborated in Sect. 4.2.
- 7.
Breaking off in the middle can be expressed using a combination of \(\mathbf{FIRSTof}\) and \(\mathbf{SEQ}\).
- 8.
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Prasetya, I.S.W.B., Dastani, M., Prada, R., Vos, T.E.J., Dignum, F., Kifetew, F. (2020). Aplib: Tactical Agents for Testing Computer Games. In: Baroglio, C., Hubner, J.F., Winikoff, M. (eds) Engineering Multi-Agent Systems. EMAS 2020. Lecture Notes in Computer Science(), vol 12589. Springer, Cham. https://doi.org/10.1007/978-3-030-66534-0_2
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