This paper presents a logic-based framework to analyze responsibility, which I refer to as intentional epistemic act-utilitarian stit theory (IEAUST). To be precise, IEAUST is used to model and syntactically characterize various modes of responsibility, where by 'modes of responsibility' I mean instances of Broersen's three categories of responsibility (causal, informational, and motivational responsibility), cast against the background of particular deontic contexts. IEAUST is obtained by integrating a modal language to express the following components of responsibility on stit models: agency, epistemic notions, intentionality, and different senses of obligation. With such a language, I characterize the components of responsibility using particular formulas. Then, adopting a compositional approach -- where complex modalities are built out of more basic ones -- these characterizations of the components are used to formalize the aforementioned modes of responsibility.

Binary decision diagrams (BDDs) are widely used to mitigate the state-explosion problem in model checking. A variation of BDDs are Zero-suppressed Decision Diagrams (ZDDs) which omit variables that must be false, instead of omitting variables that do not matter. We use ZDDs to symbolically encode Kripke models used in Dynamic Epistemic Logic, a framework to reason about knowledge and information dynamics in multi-agent systems. We compare the memory usage of different ZDD variants for three well-known examples from the literature: the Muddy Children, the Sum and Product puzzle and the Dining Cryptographers. Our implementation is based on the existing model checker SMCDEL and the CUDD library. Our results show that replacing BDDs with the right variant of ZDDs can significantly reduce memory usage. This suggests that ZDDs are a useful tool for model checking multi-agent systems.

Epistemic logic proposed by von Wright and Hintikka (see [24, 11]) is a logical formalism for reasoning about knowledge of agents. It deals with propositional knowledge, that is, the knowledge expressed as knowing that ϕ is true. In recent years, other patterns of knowledge besides knowing that are attracting increasing attention in logic community, such as knowing whether [8, 4], knowing who [3], knowing the value [2, 6], and knowing why [28] (see a survey in [27]). Motivated by different scenarios in philosophy and AI, reasoning about knowing how assertions are particularly interesting [23]. The discussion about formalizing the notion of knowing how can date back to [16, 17]. Currently, there are two main approaches of formalizing knowing how. One of them is connecting knowing how with logics of knowing that and ability (see e.g.

Multi-agent influence diagrams (MAIDs) are a popular game-theoretic model based on Bayesian networks. In some settings, MAIDs offer significant advantages over extensive-form game representations. Previous work on MAIDs has assumed that agents employ behavioural policies, which set independent conditional probability distributions over actions for each of their decisions. In settings with imperfect recall, however, a Nash equilibrium in behavioural policies may not exist. We overcome this by showing how to solve MAIDs with forgetful and absent-minded agents using mixed policies and two types of correlated equilibrium. We also analyse the computational complexity of key decision problems in MAIDs, and explore tractable cases. Finally, we describe applications of MAIDs to Markov games and team situations, where imperfect recall is often unavoidable.

Epistemic logics of intensional groups lift the assumption that membership in a group of agents is common knowledge. Instead of being represented directly as a set of agents, intensional groups are represented by a property that may change its extension from world to world. Several authors have considered versions of the intensional group framework where group-specifying properties are articulated using structured terms of a language, such as the language of Boolean algebras or of description logic. In this paper we formulate a general semantic framework for epistemic logics of structured intensional groups, develop the basic theory leading to completeness-via-canonicity results, and show that several frameworks presented in the literature correspond to special cases of the general framework.

Numerous logics have been developed to reason either about threshold-induced opinion diffusion in a network, or about similarity-driven network structure evolution, or about both. In this paper, we first introduce a logic containing different dynamic operators to capture changes that are 'asynchronous' (opinion change only, network-link change only) and changes that are 'synchronous' (both at the same time). Second, we show that synchronous operators cannot, in general, be replaced by asynchronous operators and vice versa. Third, we characterise the class of models on which the synchronous operator can be reduced to sequences of asynchronous operators.

At least since the ground-breaking work by Halpern and Moses [9], epistemic logic and interpreted runs and systems [6] are known as powerful tools for analyzing distributed systems. Distributed systems are multi-agent systems, where a set of n 2 agents, each executing some protocol, exchange messages in order to achieve some common goal. In the interpreted runs and systems framework, the set of all possible runs R (executions) of the agents in a system determines a set of Kripke models, formed by the evolution of the global state r(t) in all runs r I over time t N. Epistemic reasoning has been extended to fault-tolerant distributed systems right from the beginning, albeit restricted to benign faulty agents, i.e., agents that may only crash and/or drop messages [17, 18, 5, 9]. Actions performed by the agents when executing their protocol take place when they have accumulated specific epistemic knowledge.

The past few years have seen an uptick of interest in studying cooperative AI, that is, AI systems that are designed to be effective at cooperating. Indeed, a number of influential researchers recently argued that "[w]e need to build a science of cooperative AI... progress towards socially valuable AI will be stunted unless we put the problem of cooperation at the centre of our research" [6]. One type of cooperative behavior is joint behavior, that is, collaboration scenarios where the success of the joint action is dependent on all agents doing their parts; one agent deviating can cause the efforts of others to be ineffective. The notion of joint behavior has been studied (in much detail) under various names such as "acting together", "teamwork", "collaborative plans", and "shared plans", and highly influential models of it were developed (see, e.g., [2, 4, 10, 11, 15, 24]). Efforts were also made to engineer some of these theories into real-world joint planning systems [23, 20].

This paper presents a decentralized algorithm for solving distributed convex optimization problems in dynamic networks with time-varying objectives. The unique feature of the algorithm lies in its ability to accommodate a wide range of communication systems, including previously unsupported ones, by abstractly modeling the information exchange in the network. Specifically, it supports a novel communication protocol based on the "over-the-air" function computation (OTA-C) technology, that is designed for an efficient and truly decentralized implementation of the consensus step of the algorithm. Unlike existing OTA-C protocols, the proposed protocol does not require the knowledge of network graph structure or channel state information, making it particularly suitable for decentralized implementation over ultra-dense wireless networks with time-varying topologies and fading channels. Furthermore, the proposed algorithm synergizes with the "superiorization" methodology, allowing the development of new distributed algorithms with enhanced performance for the intended applications. The theoretical analysis establishes sufficient conditions for almost sure convergence of the algorithm to a common time-invariant solution for all agents, assuming such a solution exists. Our algorithm is applied to a real-world distributed random field estimation problem, showcasing its efficacy in terms of convergence speed, scalability, and spectral efficiency. Furthermore, we present a superiorized version of our algorithm that achieves faster convergence with significantly reduced energy consumption compared to the unsuperiorized algorithm.

Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90\% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.