Reasoning about memoryless strategies under partial observability and unconditional fairness constraints

Alternating-time Temporal Logic is a logic to reason about strategies that agents can adopt to achieve a specified collective goal. A number of extensions for this logic exist; some of them combine strategies and partial observability, some others include fairness constraints, but to the best of our knowledge no work provides a unified framework for strategies, partial observability and fairness constraints. Integration of these three concepts is important when reasoning about the capabilities of agents without full knowledge of a system, for instance when the agents can assume that the environment behaves in a fair way. We present ATLK<inf>irF</inf>, a logic combining strategies under partial observability in a system with fairness constraints on states. We introduce a model-checking algorithm for ATLK<inf>irF</inf> by extending the algorithm for a full-observability variant of the logic and we investigate its complexity. We validate our proposal with an experimental evaluation.