Hedonic games provide a model of coalition formation in which a set of agents is partitioned into coalitions and the agents have preferences over which set they belong to. Recently, Aziz et. al. (2014) have initiated the study of hedonic games with dichotomous preferences, where each agent either approves or disapproves of a given coalition. In this work, we study the computational complexity of questions related to finding optimal and stable partitions in dichotomous hedonic games under various ways of restricting and representing the collection of approved coalitions. Encouragingly, many of these problems turn out to be polynomial-time solvable. In particular, we show that an individually stable outcome always exists and can be found in polynomial time. We also provide efficient algorithms for cases in which agents approve only few coalitions, in which they only approve intervals, and in which they only approve sets of size 2 (the roommates case). These algorithms are complemented by NP-hardness results, especially for representations that are very expressive, such as in the case when agents' goals are given by propositional formulas.

We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying crowdsourcing in these settings is that agents may misreport their reviews of others to increase their chances of being selected. We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. We then show, using a detailed experiment with parameter values derived from target real world domains, that our mechanism performs better on average, and in the worst case, than other strategyproof mechanisms in the literature.

In multiagent systems, if interactions are based on trust, trustworthy trustees will have a greater impact on the results of interactions. Consequently, building a high trust may be an advantage for rational trustees. This work describes a trust establishment model that goes beyond trust evaluation to outline actions to direct trustees (instead of trusters). The model uses the number of transactions performed by trusters. A trustee will adjust its performance, depending on the average number of transactions carried out by that truster, relative to the mean number of transactions performed by all trusters interacting with this trustee. The proposed model does not depend on direct feedback, nor does it rely on current reputation of trustees in the community. Simulation results indicate that trustees empowered with the proposed model can be selected more by trusters.

As robots evolve into fully autonomous agents, settings involving human-robot teams will evolve into human-robot societies, where multiple independent agents and teams, both humans and robots, coexist and work in harmony. Given such a scenario, the question we ask is - How can two or more such agents dynamically form coalitions or teams for mutual benefit with minimal prior coordination? In this work, we provide a game theoretic solution to address this problem. We will first look at a situation with full information, provide approximations to compute the extensive form game more efficiently, and then extend the formulation to account for scenarios when the human is not totally confident of its potential partner's intentions. Finally we will look at possible extensions of the game, that can capture different aspects of decision making with respect to ad-hoc coalition formation in human-robot societies.

Condorcet winning sets are a set-valued generalization of the well-known concept of a Condorcet winner. As supersets of Condorcet winning sets are always Condorcet winning sets themselves, an interesting property of preference profiles is the size of the smallest Condorcet winning set they admit. This smallest size is called the Condorcet dimension of a preference profile. Since little is known about profiles that have a certain Condorcet dimension, we show in this paper how the problem of finding a preference profile that has a given Condorcet dimension can be encoded as a satisfiability problem and solved by a SAT solver. Initial results include a minimal example of a preference profile of Condorcet dimension 3, improving previously known examples both in terms of the number of agents as well as alternatives. Due to the high complexity of such problems it remains open whether a preference profile of Condorcet dimension 4 exists.

This paper considers the problem of cooperation between self-interested agents in acquiring better information regarding the nature of the different options and opportunities available to them. By sharing individual findings with others, the agents can potentially achieve a substantial improvement in overall and individual expected benefits. Unfortunately, it is well known that with self-interested agents equilibrium considerations often dictate solutions that are far from the fully cooperative ones, hence the agents do not manage to fully exploit the potential benefits encapsulated in such cooperation. In this paper we introduce, analyze and demonstrate the benefit of five methods aiming to improve cooperative information gathering. Common to all five that they constrain and limit the information sharing process. Nevertheless, the decrease in benefit due to the limited sharing is outweighed by the resulting substantial improvement in the equilibrium individual information gathering strategies. The equilibrium analysis given in the paper, which, in itself is an important contribution to the study of cooperation between self-interested agents, enables demonstrating that for a wide range of settings an improved individual expected benefit is achieved for all agents when applying each of the five methods.

Autonomous agents require interfaces to define their interactions with humans. The coupling between agents and humans is often limited, with disjoint goals between the agent interface and its associated autonomous components. This leads to a gap in human interaction relative to agent capabilities. We seek to aid interface designs by clarifying agent capabilities within an interface context. A taxonomy was developed that can help elucidate the agent’s affordances and constraints that guide interface design. Moreover, the descriptors employed in the taxonomy can serve as a common language to support dialog between agent and interface developers, resulting in improved autonomous systems that support human-autonomy coordination.

Coordination with an unknown human teammate is a notable challenge for cooperative agents. Behavior of human players in games with cooperating AI agents is often sub-optimal and inconsistent leading to choreographed and limited cooperative scenarios in games. This paper considers the difficulty of cooperating with a teammate whose goal and corresponding behavior change periodically. Previous work uses Bayesian models for updating beliefs about cooperating agents based on observations. We describe belief models for on-line planning, discuss tuning in the presence of noisy observations, and demonstrate empirically its effectiveness in coordinating with inconsistent agents in a simple domain. Further work in this area promises to lead to techniques for more interesting cooperative AI in games.

We introduce Playspecs, an application of omega-regular expressions to specifying play traces (sequences of game states or events unfolding over time). This connects the automated analysis and model checking of games to the literature on formal software verification via Bu ̈chi automata. We show how to define desirable or undesirable sequences of game events with Playspecs and how associated algorithms can find examples (or prove the impossibility) of such sequences. Playspecs have two main benefits over existing techniques for specifying the behaviors of a game over time. First, they offer a scalable commitment to formal modeling: the same Playspecs can filter existing traces gathered by telemetry, search for satisfying traces using existing game code, or drive formal verification when paired with a logical model of a game. Second, Playspecs' syntax can be customized for the game engine or game in question so designers may write specifications using their game's native vocabulary. We define Playspecs' syntax and semantics (modulo gamespecific customizations) and outline algorithms for each of the applications mentioned above, providing examples from the social simulation game Prom Week and the puzzle game engine PuzzleScript.

My research goal involves simultaneously addressing statistical and computational tradeoffs encountered in modern data analysis and high-dimensional machine learning (eg: hypothesis testing, regression, classification). My future interests include incorporating additional constraints like privacy or communication, and settings involving hidden utilities of multiple cooperative agents or competitive adversaries.