Yuqing Tang

Using argumentation to reason about trust and belief1

Trust is a mechanism for managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multi-agent systems and related fields such as the semantic web. Here, we introduce a formal system of argumentation that can be used to reason using information about trust. This system is described as a set of graphs, which makes it possible to combine our approach with conventional representations of trust between individuals where the relationships between individuals are given in the form of a graph. The resulting system can easily relate the grounds of an argument to the agent that supplied the information, and can be used as the basis to compute Dungian notions of acceptability that take trust into account. We explore some of the properties of these argumentation graphs, examine the computation of trust and belief in the graphs and illustrate the capabilities of the system on an example from the trust literature.

Argumentation-based dialogues for deliberation

This paper presents an argumentation-based approach to deliberation, the process by which two or more agents reach a consensus on a course of action. The kind of deliberation we are interested in combines both the selection of an overall goal, the reduction of this goal into sub-goals, and the formation of a plan to achieve the overall goal. We develop a mechanism for doing this and then proceed to describe how it can be integrated into a system of argumentation to provide a sound and complete deliberation system, before showing how the same process can be achieved through a multi-agent dialogue.

Additive preconditioning and aggregation in matrix computations

We combine our novel SVD-free additive preconditioning with aggregation and other relevant techniques to facilitate the solution of a linear system of equations and other fundamental matrix computations. Our analysis and experiments show the power of our algorithms, guide us in selecting most effective policies of preconditioning and aggregation, and provide some new insights into these and related subjects. Compared to the popular SVD-based multiplicative preconditioners, our additive preconditioners are generated more readily and for a much larger class of matrices. Furthermore, they better preserve matrix structure and sparseness and have a wider range of applications (e.g., they facilitate the solution of a consistent singular linear system of equations and of the eigenproblem).

Root-Finding with Eigen-Solving

We survey and extend the recent progress in polynomial root-finding via eigen-solving for highly structured generalized companion matrices. We cover the selection of eigen-solvers and matrices and show the benefits of exploiting matrix structure. No good estimates for the rate of global convergence of the eigen-solvers are known, but according to ample empirical evidence it is sufficient to use a constant number of iteration steps per eigenvalue. If so, the resulting root-finders are optimal up to a constant factor because they use linear arithmetic time per step and perform with a constant (double) precision. Some by-products of our study are of independent interest. The algorithms can be extended to solving secular equations

A model for integrating dialogue and the execution of joint plans

Coming up with a plan for a team that operates in a non-deterministic environment is a complex process, and the problem is further complicated by the need for team members to communicate while the plan is being executed. Such communication is required, for example, to make sure that information critical to the plan is passed in time for it to be useful. In this paper we present a model for constructing joint plans for a team of agents that takes into account their communication needs. The model builds on recent developments in symbolic non-deterministic planning, ideas that have not previously been applied to this problem.

Modeling human education data: from equation-based modeling to agent-based modeling

Agent-based simulation is increasingly used to analyze the performance of complex systems. In this paper we describe results of our work on one specific agent-based model, showing how it can be validated against the equation-based model from which it was derived, and demonstrating the extent to which it can be used to derive additional results over and above those that the equation-based model can provide. The agent-based model that we build deals with human capital, the number of years of formal schooling that an individual chooses to undertake. For verification, we show that our agent-based model makes similar predictions about the growth in inequality - that is the growth of the variance in human capital across the population - as th equation-based model from which it is derived. In addition, we show that our model can make predictions about the change in human capital from generation to generation that are beyond the equation-based model.

Argumentation-Based multi-agent dialogues for deliberation

This paper presents an argumentation-based approach to deliberation, the process by which two or more agents reach a consensus on a course of action. The kind of deliberation that we are interested in is a process that combines both the selection of an overall goal, the reduction of this goal into sub-goals, and the formation of a plan to achieve the overall goal. We develop a mechanism for doing this, describe how this mechanism can be integrated into a system of argumentation to provide a sound and complete deliberation system, and show how the same process can be achieved through a multi-agent dialogue.

Eigen-solving via reduction to DPR1 matrices

Highly effective polynomial root-finders have been recently designed based on eigen-solving for DPR1 (that is diagonal + rank-one) matrices. We extend these algorithms to eigen-solving for the general matrix by reducing the problem to the case of the DPR1 input via intermediate transition to a TPR1 (that is triangular + rank-one) matrix. Our transforms use substantially fewer arithmetic operations than the QR classical algorithms but employ non-unitary similarity transforms of a TPR1 matrix, whose representation tends to be numerically unstable. We, however, operate with TPR1 matrices implicitly, as with the inverses of Hessenberg matrices. In this way our transform of an input matrix into a similar DPR1 matrix partly avoids numerical stability problems and still substantially decreases arithmetic cost versus the QR algorithm.

A framework for using trust to assess risk in information sharing

In this paper we describe a decision process framework allowing an agent to decide what information it should reveal to its neighbours within a communication graph in order to maximise its utility. We assume that these neighbours can pass information onto others within the graph, and that the communicating agent gains and loses utility based on the information which can be inferred by specific agents following the original communicative act. To this end, we construct an initial model of information propagation and describe an optimal decision procedure for the agent.

Using Cognitive Architectures to Study Issues in Team Cognition in a Complex Task Environment

Cognitive social simulation is a computer simulation technique that aims to improve our understanding of the dynamics of socially-situated and socially-distributed cognition. This makes cognitive social simulation techniques particularly appealing as a means to undertake experiments into team cognition. The current paper reports on the results of an ongoing effort to develop a cognitive social simulation capability that can be used to undertake studies into team cognition using the ACT-R cognitive architecture. This capability is intended to support simulation experiments using a team-based problem solving task, which has been used to explore the effect of different organizational environments on collective problem solving performance. The functionality of the ACT-R-based cognitive social simulation capability is presented and a number of areas of future development work are outlined. The paper also describes the motivation for adopting cognitive architectures in the context of social simulation experiments and presents a number of research areas where cognitive social simulation may be useful in developing a better understanding of the dynamics of team cognition. These include the use of cognitive social simulation to study the role of cognitive processes in determining aspects of communicative behavior, as well as the impact of communicative behavior on the shaping of task-relevant cognitive processes (e.g., the social shaping of individual and collective memory as a result of communicative exchanges). We suggest that the ability to perform cognitive social simulation experiments in these areas will help to elucidate some of the complex interactions that exist between cognitive, social, technological and informational factors in the context of team-based problem-solving activities.