Cooperation among automated agents is becoming increasingly important in various artificial intelligence applications. Coalitional (i.e., cooperative) game theory supplies conceptual and mathematical tools useful in the analysis of such interactions, and in particular in the achievement of stable outcomes among self-interested agents. Here, we study the minimal external subsidy required to stabilize the core of a coalitional game. Following the Cost of Stability (CoS) model introduced by Bachrach et al. [2009a], we give tight bounds on the required subsidy under various restrictions on the social structure of the game. We then compare the extended core induced by subsidies with the least core of the game, proving tight bounds on the ratio between the minimal subsidy and the minimal demand relaxation that each lead to stability.

Multi-agent path planning is a challenging problem with numerous real-life applications, including robotics, logistics, military operations planning, disaster rescue, and computer games. We look at navigating large numbers of mobile units to their targets on navigation graphs such as grid maps. The size of problems examined is significantly larger than can be handled using optimal multi-agent pathfinding algorithms in practice. We introduced MAPP, a tractable algorithm for multi-agent path planning on undirected graphs. MAPP and its extended versions are complete on well specified and tractably testable classes of problems. They have low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. Experiments on realistic game grid maps, with uniformly randomly generated start and target locations for each unit, show MAPP as a state-of-the-art multi-agent pathfinding algorithm in terms of scalability and success ratio (i.e., percentage of solved units). Even on challenging scenarios with 2000 units, MAPP solves 92% to 99.7% of units. FAR and WHCA*, two fast but incomplete algorithms that were previously state-of-the-art in terms of scalability, solve as few as 17.5% and 12.3% of these problems. The quality of MAPP's solutions is empirically analyzed using multiple quality criteria: total travel distance, makespan, and sum of actions (including move and wait actions). MAPP is competitive in terms of solution quality and speed with FAR and WHCA*. MAPP further provides the formal characterizations that FAR and WHCA* lack, on problems it can solve as well as low-polynomial upper bounds on the resources required. As optimal algorithms have limited scalability, we evaluated the solution quality of suboptimal algorithms using lower bounds of optimal values. We showed that MAPP's solutions have a reasonable quality. For example, MAPP's total travel distance is on average 19% longer than a lower bound on the optimal value.

My work focuses on building computational agents that assist people in managing their activities in environments in which tempo and complexity outstrip people’s cognitive capacity,such as in coordinating rescue teams in the aftermath of a disaster, or in helping people with dementia manage their everyday lives. A critical challenge faced in such environments is not only that individuals must factor complicated constraints into deciding how and when to act on their own goals, but also that their decisions are further constrained by choices made by others with whom they interact, such as between cooperating teams in disaster relief or between patients and caregivers in an assisted-living facility. An additional challenge in such situations is that the interests of individuals, such as privacy and autonomy, along with slow, costly, uncertain,or otherwise problematic communication may further limitindividuals’ abilities to work together. My work assumes that a computational agent is associated with each individual, and that these agents will work together efficiently to manage individual and joint activities, while maintaining autonomy and privacy to the extent possible.

Cake cutting is a playful name for the problem of fairly dividing a heterogeneous divisible good among a set of agents. The agent valuations for different pieces of cake are typically assumed to be additive. However, in certain practical settings this assumption is invalid because agents may not have positive value for arbitrarily small "crumbs" of cake. In this paper, we propose a new, more expressive model of agent valuations that captures this feature. We present an approximately proportional algorithm for any number of agents that have such expressive valuations. The algorithm is optimal in the sense that no other algorithm can guarantee a greater worst-case degree of proportionality. We also design an optimal approximately proportional and fully envy-free algorithm for two agents.

We address the issue of manipulating games through communication. In the specific setting we consider (a variation of Boolean games), we assume there is some set of environment variables, the value of which is not directly accessible to players; each player has their own beliefs about these variables, and makes decisions about what actions to perform based on these beliefs. The communication we consider takes the form of (truthful) announcements about the value of some environment variables; the effect of an announcement about some variable is to modify the beliefs of the players who hear the announcement so that they accurately reflect the value of the announced variables. By choosing announcements appropriately, it is possible to perturb the game away from certain rational outcomes and towards others. We specifically focus on the issue of stabilisation: making announcements that transform a game from having no stable states to one that has stable configurations.

We propose a logical framework to represent and reason about agent interactions in normative systems. Our starting point is a dynamic logic of propositional assignments whose satisfiability problem is PSPACE-complete. We show that it embeds Coalition Logic of Propositional Control CL-PC and that various notions of ability and capability can be captured in it. We illustrate it on a water resource management case study. Finally, we show how the logic can be easily extended in order to represent constitutive rules which are also an essential component of the modelling of social reality.

Gjerstad and Dickhaut, 1998; Nicolaisen et al., 2001] and a market selection strategy which is mainly based on the history This paper describes the strategies used by of the trader's profit made with each specialist. AstonCAT-Plus, the post-tournament version of A CAT game lasts a number of days (500 days in CATthe specialist designed for the TAC Market Design 2010). Each day consists of a number of trading rounds, Tournament 2010. It details how AstonCATwhich each lasts for a known constant length of time. The Plus accepts shouts, clears market, sets transaction daily evaluation of the specialists is based on three metrics: prices and charges fees. Through empirical evaluation, (1) market share, which is the percentage of the total traders' we show that AstonCAT-Plus not only outperforms population registered in the market; (2) profit share, which is AstonCAT (tournament version) significantly the ratio of the daily profit a specialist obtains to the profit of but also achieves the second best overall all specialists and (3) transaction success rate (TSR), which score against some top entrants of the competition.

The generation of route descriptions is a fundamental task of navigation systems. A particular problem in this context is to identify routes that can easily be described and processed by users. In this work, we present a framework for representing route networks with the qualitative information necessary to evaluate and optimize route descriptions with regard to ambiguities in them. We identify different agent models that differ in how agents are assumed to process route descriptions while navigating through route networks. Further, we analyze the computational complexity of matching route descriptions and paths in route networks in dependency of the agent model. Finally we empirically evaluate the influence of the agent model on the optimization and the processing of route instructions.

Metaphorical interpretation and affect detection using context profiles from open-ended text input are challenging in affective language processing field. In this paper, we explore recognition of a few typical affective metaphorical phenomena and context-based affect sensing using the modeling of speakers’ improvisational mood and other participants’ emotional influence to the speaking character under the improvisation of loose scenarios. The overall updated affect detection module is embedded in an AI agent. The new developments have enabled the AI agent to perform generally better in affect sensing tasks. The work emphasizes the conference themes on affective dialogue processing, human-agent interaction and intelligent user interfaces.

Deliberation itself can be a computationally costly process and requires This paper shows how belief revision techniques an appropriate intention reconsideration policy which can be used in Defeasible Logic to change rulebased helps the agent to deliberate only when necessary. In this picture, theories characterizing the deliberation process it is still overlooked the problem of changing intentions of cognitive agents. We discuss intention reconsideration not because of the change of beliefs, but because the normative as a strategy to make agents compliant constraints require to do so.