In game theory, as well as in the semantics of game logics, a strategy can be represented by any function from states of the game to the agent's actions. That makes sense from the mathematical point of view, but not necessarily in the context of human behavior. This is because humans are quite bad at executing complex plans, and rather unlikely to come up with such plans in the first place. A similar concern applies to artificial agents with limited memory and/or computational power. In this paper, we adopt the view of bounded rationality, and look at "simple" strategies in specification of agents' abilities. We formally define what "simple" means, and propose a variant of alternating-time temporal logic that takes only such strategies into account. We also study the model checking problem for the resulting semantics of ability. After that, we turn to the broader issue of natural strategic abilities in concurrent games with LTL-definable winning conditions, and study a number of decision problems based on surely winning and Nash equilibrium. We show that, by adopting the view of bounded rationality based on natural strategies, we significantly decrease the complexity of rational verification for multi-agent systems.

We investigate the verification of Multi-agent Systems against strategic properties expressed in Alternating-time Temporal Logic under the assumptions of imperfect information and perfect recall. To this end, we develop a three-valued semantics for concurrent game structures upon which we define an abstraction method. We prove that concurrent game structures with imperfect information admit perfect information abstractions that preserve three-valued satisfaction. Further, we present a refinement procedure to deal with cases where the value of a specification is undefined. We illustrate the overall procedure in a variant of the Train Gate Controller scenario under imperfect information and perfect recall.

The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic AT L, hence AT L∗ , under perfect recall and imperfect information is known to be undecidable. To tackle this problem, in this paper we investigate a notion of bounded recall under incomplete information. We present a novel three-valued semantics for AT L∗ in this setting and analyse the corresponding model checking problem. We show that the three-valued semantics here introduced is an approximation of the classic two-valued semantics, then give a sound, albeit partial, algorithm for model checking two-valued perfect recall via its approximation as three-valued bounded recall. Finally, we extend MCMAS, an open-source model checker for AT L and other agent specifications, to incorporate bounded recall; we illustrate its use and present experimental results.

A major challenge for logics for strategies is represented by their verification in contexts of imperfect information. In this contribution we advance the state of the art by approximating the verification of Alternating-time Temporal Logic (ATL) under imperfect information by using perfect information and a three-valued semantics. In particular, we develop novel automata-theoretic techniques for the linear-time logic LTL, then apply these to finding “failure” states, where the ATL specification to be model checked is undefined. Such failure states can then be fed into a refinement procedure, thus providing a sound, albeit incomplete, verification procedure.

In this paper we introduce Strategy Logic with simple goals (SL[SG]), a fragment of Strategy Logic that strictly extends the well-known Alternating-time Temporal Logic ATL by introducing arbitrary quantification over the agents' strategies.  Our motivation comes from game-theoretic applications, such as expressing Stackelberg equilibria in games, coercion in voting protocols, as well as module checking for simple goals. Most importantly, we prove that the model checking problem for SL[SG] is PTIME-complete, the same as ATL. Thus, the extra expressive power comes at no computational cost as far as verification is concerned.

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Strategy Logic (SL) is a logical formalism for strategic reasoning in multi-agent systems. Its main feature is that it has variables for strategies that are associated to specific agents using a binding operator. In this paper we introduce Graded Strategy Logic (GradedSL), an extension of SL by graded quantifiers over tuples of strategy variables, i.e., "there exist at least g different tuples (x 1 , ..., x n ) of strategies" where g is a cardinal from the set N ∪ {ℵ 0 , ℵ 1 , 2 ℵ0 }. We prove that the model-checking problem of GradedSL is decidable. We then turn to the complexity of fragments of GradedSL. When the g's are restricted to finite cardinals, written Graded N SL, the complexity of model-checking is no harder than for SL, i.e., it is non-elementary in the quantifier-block rank. We illustrate our formalism by showing how to count the number of different strategy profiles that are Nash equilibria (NE). By analysing the structure of the specific formulas involved, we conclude that the important problem of checking for the existence of a unique NE can be solved in 2ExpTime, which is not harder than merely checking for the existence of such an equilibrium.

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