Translations between different nonmonotonic formalisms always have been an important topic in the field, in particular to understand the knowledge-representation capabilities those formalisms offer. We provide such an investigation in terms of different semantics proposed for abstract argumentation frameworks, a nonmonotonic yet simple formalism which received increasing interest within the last decade. Although the properties of these different semantics are nowadays well understood, there are no explicit results about intertranslatability. We provide such translations wrt.

We consider the problem of dynamic self-reconfiguration in a modular self-reconfigurable robot (MSR). Previous MSR self-reconfiguration approaches search for new configurations only within the modules of the MSR that needs reconfiguration. In contrast, we describe a technique where an MSR that needs to reconfigure communicates with other MSRs in its vicinity to determine if modules can be shared from other MSRs, and then determines the best possible configuration among the combined set of modules. We model the MSR self-reconfiguration problem as a coalition structure generation problem within a coalition game theoretic framework. We formulate the coalition structure generation problem as a planning problem in the presence of uncertainty and propose an MDP-based algorithm to solve it. We have implemented our algorithm within an MSR called ModRED that is simulated on the Webots simulation platform. Our results show that using our self-reconfiguration algorithm, when an MSR needs to reconfigure, a new configuration that is within 5-7% of the globally optimal configuration can be determined. We have also shown that our algorithm performs comparably with another existing algorithm for determining optimal coalition structure.

Being able to compactly represent large state spaces is crucial in solving a vast majority of practical stochastic planning problems. This requirement is even more stringent in the context of multi-agent systems, in which the world to be modeled also includes the mental state of other agents. This leads to a hierarchy of beliefs that results in a continuous, unbounded set of possible interactive states, as in the case of Interactive POMDPs. In this paper, we describe a novel representation for interactive belief hierarchies that combines first-order logic and probability. The semantics of this new formalism is based on recursively partitioning the belief space at each level of the hierarchy; in particular, the partitions of the belief simplex at one level constitute the vertices of the simplex at the next higher level. Since in general a set of probabilistic statements only partially specifies a probability distribution over the space of interest, we adopt the maximum entropy principle in order to convert it to a full specification.

Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully observable stochastic game. MGIIs represents the most general tractable model for multi-agent reinforcement learning to date.

The task of coordinating hundreds of mobile robots in one of Kiva System's warehouses presents many challenging multi-agent resource allocation problems. The resources include things like inventory, open orders, small shelving units, and the robots themselves. The types of resources can be classified by whether they are consumable, recycled, or scheduled. Further, the global optimization problem can be broken down into more manageable sub-problems, some of which map to (hard) versions of well known computational problems, but with a dynamic, temporal twist.

We introduce the notion of strategic uncertainty for boundedly rational, non-myopic agents as an analog to the equilibrium selection problem in classical game theory. We then motivate the need for and feasibility of addressing strategic uncertainty and present an algorithm that produces decisions that are robust to it. Finally, we show how agents' rationality levels and planning horizons alter the robustness of their decisions.

We propose a mechanism to learn communicative action structure to analyze adversarial reasoning patterns in customer complaints. An efficient way to assist customers and companies is to reuse previous experience with similar agents. A formal representation of customer complaints and a machine learning technique for handling scenarios of interaction between conflicting human agents are proposed. It is shown that analyzing the structure of communicative actions without context information is frequently sufficient to advise on complaint resolution strategies. Therefore, being domain-independent, the proposed machine learning technique is a good complement to a wide range of customer response management applications where formal treatment of inter-human interactions is required.

Model-driven visualization (MDV) is a novel framework that supports more effective, intelligent user interfaces to improve decision making in complex environments by coupling cognitive and perceptual theories of information processing with advanced artificial intelligence methods. It embeds empirical and theory driven approaches for identifying and prioritizing data based on the information requirements and needs of the human decision maker within intelligent agents. The agents automatically deliver and present information based on its likely value using visualizations that best convey that information to the user(s) of the system. Agents also reason about the context and constraints of the user, environment, and display to enable a higher degree of personalization within an interactive user interface (e.g., by drawing a user’s attention to interesting aspects of the data such as trends, anomalies, and patterns). We apply cognitive systems engineering processes to help identify the information available to individuals and/or teams, where it resides, where it is needed, and ultimately how to create the mappings required in connecting critical information to those who need it with innovative visualizations that most effectively support the end user. This paper describes the application of MDV to intelligently deliver timely, mission-critical information by adapting a Common Tactical Picture (CTP) display used for maritime situation awareness, threat assessment, and decision support.

In many multiagent scenarios, groups of participants (known as coalitions) may attempt to cooperate, seeking to increase the benefits realized by the members. Depending on the scenario, such cooperation may be benign, or may be unwelcome or even forbidden (often called collusion). Coalitions can present a problem for many multiagent systems, potentially undermining the intended operation of systems. In this paper, we present a technique for detecting the presence of coalitions (malicious or otherwise), and identifying their members. Our technique employs clustering in benefit space, a high-dimensional feature space reflecting the benefit flowing between agents, in order to identify groups of agents who are similar in terms of the agents they are favoring. A statistical approach is then used to characterize candidate clusters, identifying as coalitions those groups that favor their own members to a much greater degree than the general population. We believe that our approach is applicable to a wide range of domains. Here, we demonstrate its effectiveness within a simulated marketplace making use of a trust and reputation system to cope with dishonest sellers. Many trust and reputation proposals readily acknowledge their ineffectiveness in the face of collusion, providing one example of the importance of the problem. While certain aspects of coalitions have received significant attention (e.g., formation, stability, etc.), relatively little research has focused on the problem of coalition identification. We believe our research represents an important step towards addressing the challenges posed by coalitions.

An ad hoc team setting is one in which teammates must work together to obtain a common goal, but without any prior agreement regarding how to work together. In this paper we present a role-based approach for ad hoc teamwork, in which each teammate is inferred to be following a specialized role that accomplishes a specific task or exhibits a particular behavior. In such cases, the role an ad hoc agent should select depends both on its own capabilities and on the roles currently selected by the other team members. We formally define methods for evaluating the influence of the ad hoc agent's role selection on the team's utility, leading to an efficient calculation of the role that yields maximal team utility. In simple teamwork settings, we demonstrate that the optimal role assignment can be easily determined. However, in complex environments, where it is not trivial to determine the optimal role assignment, we examine empirically the best suited method for role assignment. Finally, we show that the methods we describe have a predictive nature. As such, once an appropriate assignment method is determined for a domain, it can be used successfully in new tasks that the team has not encountered before and for which only limited prior experience is available.