The cake cutting problem models the fair division of a heterogeneous good between multiple agents. Previous work assumes that each agent derives value only from its own piece. However, agents may also care about the pieces assigned to other agents; such externalities naturally arise in fair division settings. We extend the classical model to capture externalities, and generalize the classical fairness notions of proportionality and envy-freeness. Our technical results characterize the relationship between these generalized properties, establish the existence or nonexistence of fair allocations, and explore the computational feasibility of fairness in the face of externalities.

We define a family of epistemic extensions of Halpern-Shoham logic for reasoning about temporal-epistemic properties of multi-agent systems. We exemplify their use and study the complexity of their model checking problem. We show a range of results ranging from PTIME to PSPACE-hard depending on the logic considered.

This research proposes the use of imitation based learning to build collaborative strategies for a team of agents. Imitation based learning involves learning from an expert by observing her demonstrating a task and then replicating it. This mechanism makes it extremely easy for a knowledge engineer to transfer knowledge to a software agent via human demonstrations. This research aims to apply imitation to learn not only the strategy of an individual agent but also the collaborative strategy of a team of agents to achieve a common goal. The effectiveness of the proposed methodology is being assessed in the domain of RoboCup Soccer Simulation 3D which is a promising platform to address many of the complex real-world problems and offers a truly dynamic, stochastic, and partially-observable environment.

Just as conventional institutions are organisationalstructures for coordinating the activities of multipleinteracting individuals, electronic institutions providea computational analogue for coordinating theactivities of multiple interacting software agents.In this paper, we argue that open multi-agent systemscan be effectively designed and implementedas electronic institutions, for which we provide acomprehensive computational model. More specifically,the paper provides an operational semanticsfor electronic institutions, specifying the essentialdata structures, the state representation and the keyoperations necessary to implement them.

In this paper, we state the challenges of high-level program execution in multi-agent settings. We first introduce high-level program execution and the related work. Then we describe the completed work, the future work and its approaches. We conclude with the expected contributions of our research.

We consider the problem of updating a multi-agent system with a set of conditional norms. A norm comes into effect when its condition becomes true, and imposes either an obligation or a prohibition on an agent which remains in force until a state satisfying a deadline condition is reached. If the norm is violated, a sanction is imposed on the agent. We define a notion of a normative update of a multi-agent system by a set of conditional norms, and study the problem of checking whether the agent(s) can bring about a state satisfying a property without incurring a specified number of sanctions.

Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social causality and responsibility judgment according to an agent’s causal knowledge and observations of interaction. The proposed model is also empirically validated via experimental study.

Dynamic epistemic logic (DEL) provides a very expressive framework for multi-agent planning that can deal with nondeterminism, partial observability, sensing actions, and arbitrary nesting of beliefs about other agents’ beliefs. However, as we show in this paper, this expressiveness comes at a price. The planning framework is undecidable, even if we allow only purely epistemic actions (actions that change only beliefs, not ontic facts). Undecidability holds already in the S5 setting with at least 2 agents, and even with 1 agent in S4. It shows that multi-agent planning is robustly undecidable if we assume that agents can reason with an arbitrary nesting of beliefs about beliefs. We also prove a corollary showing undecidability of the DEL model checking problem with the star operator on actions (iteration).

Subset space logics have been introduced and studied as a framework for reasoning about a notion of effort in epistemic logic. The seminal Subset Space Logic (SSL) by Moss and Parikh modeled a single agent, and most work in this area has focused on different extensions of the language, or different model classes resulting from restrictions on subset spaces, while still keeping the single-agent assumption. In this paper we argue that the few existing attempts at multi-agent versions of SSL are unsatisfactory, and propose a new multi-agent subset space logic which is a natural extension of single-agent SSL. The main results are a sound and complete axiomatization of this logic, as well as an alternative and equivalent relational semantics.

Team formation is a critical step in deploying a multi-agent team. In some scenarios, agents coordinate by voting continuously. When forming such teams, should we focus on the diversity of the team or on the strength of each member? Can a team of diverse (and weak) agents outperform a uniform team of strong agents? We propose a new model to address these questions. Our key contributions include: (i) we show that a diverse team can overcome a uniform team and we give the necessary conditions for it to happen; (ii) we present optimal voting rules for a diverse team; (iii) we perform synthetic experiments that demonstrate that both diversity and strength contribute to the performance of a team; (iv) we show experiments that demonstrate the usefulness of our model in one of the most difficult challenges for Artificial Intelligence: Computer Go.