State abstraction optimizes decision-making by ignoring irrelevant environmental
information in reinforcement learning with rich observations. Nevertheless, recent …

In today's world of connectivity, various domains use different multi-sensor Internet of Things
(IoT) and Intelligent Systems (IS) applications. These applications involve extensive …

In multi-agent system design and reasoning, strategy logics and formal verification play
pivotal roles. Numerous logic formalisms have been introduced alongside the …

In logics for the strategic reasoning the main challenge is represented by their verification in
contexts of imperfect information and perfect recall. In this work, we show a technique to …

The model checking problem for multi-agent systems against Strategy Logic specifications is
known to be non-elementary. On this logic several fragments have been defined to tackle …

In the dynamic landscape of Internet of Things (IoT) applications within multi-source data
environments, ensuring the reliability and correctness of system communications has …

Effective communication among autonomous agents is crucial for coordination and solving
complex tasks within multi-agent systems. To formalize interactions between agents, social …

We present a general framework for abstracting agent behavior in multi-agent synchronous
games in the situation calculus, which provides a first-order representation of the state and …

Although Reinforcement Learning (RL) algorithms acquire sequential behavioral patterns
through interactions with the environment, their effectiveness in noisy and high-dimensional …

In this paper, we propose a novel formalism called Probabilistic Obstruction Temporal Logic
(POTL), which extends Obstruction Logic (OL) by incorporating probabilistic elements. POTL …