In heterogeneous multi-agent systems trust is necessary to improve interactions by enabling agents to choose good partners. Most trust models work by taking, in addition to direct experiences, other agents' communicated evaluations into account. However, in an open MAS other agents may use different trust models and the evaluations they communicate are based on different principles: as such they are meaningless without some form of alignment. My doctoral research gives a formal definition of this problem and proposes two methods of achieving an alignment.

The scalability of algorithms for solving Markov Decision Processes (MDPs) has been a limiting factor for MDPs as a modeling tool. This dissertation develops theoretical and empirical techniques for solving larger MDPs than was possible before, and aims to demonstrate the achieved progress by applying these new algorithms to a real-world problem.

My research proposes to speed up grid-based pathfinding by identifying and eliminating symmetric path segments from the search space. Two paths are said to be symmetric if they are identical save for the order in which the individual moves (or steps) occur. To deal with path symmetries I decompose an arbitrary grid map into a set of empty rectangles and remove from each rectangle all interior nodes and possibly some from along the perimeter. A series of macro edges are then added between selected pairs of remaining nodes in order to facilitate provably optimal traversal through each rectangle. The new algorithm, Rectangular Symmetry Reduction (RSR), can speed up A* search by up to 38 times on a range of uniform cost maps taken from the literature. In addition to being fast and optimal, RSR requires no significant extra memory and is largely orthogonal all existing speedup techniques. When compared to the state of the art, RSR often shows significant improvement across a range of benchmarks.

HAPTIC is grounded in game theory and probabilistic modeling. It has been proved that My thesis contributes to the field of multi-agent HAPTIC agents learn other agents' behaviors reliably using systems by proposing a novel trust-based decision direct observations. One shortcoming of HAPTIC is that it model for supply chain management.

Distributed Constraint Optimization Problems (DCOPs) can be optimally solved by distributed search algorithms, such as ADOPT and BnB-ADOPT. In centralized solving, maintaining soft arc consistency during search has proved to be beneficial for performance. In this thesis we aim to explore the maintenance of different levels of soft arc consistency in distributed search when solving DCOPs.

Model checking is one of the most effective techniques in automated system verification. Although this technique can handle complex verifications, model checking tools usually do not give any suggestions on how to repair inconsistent system models. In this paper, we show that approaches developed to update models of Computation Tree Logic (CTL) cannot deal with all kinds of changes. We introduce the concept of CTL model revision: an approach based on belief revision to handle system inconsistency in a static context.

This paper describes work related to stochastic modeling and decision-theoretic (DT) planning methods applicable to the real-world domain of academic advising.

Quantitative prediction problems involving both spatial and temporal components have appeared prominently in several disparate research areas including finance, supply chain management, and civil engineering. Unfortunately, either the spatial or temporal aspect tends to dominate the other in many prediction formulations. We briefly examine the underlying formulations used in spatial and temporal prediction. Then, we outline a method that combines these approaches and improves prediction results in high-dimensional economic domains by integrating multivariate and time series techniques which require minimal tuning but achieve superior performance compared to previous methods. We present preliminary results in the context of the Trading Agent Competition for Supply Chain Management.

Finally, explore possible methods for decision making in general, have received a lot uses of combinatorial aggregation in sequential voting, of attention in the AI community in recent years. The reasons and discuss theoretical generalisations to more complex logical for this focus are clear: SCT provides tools for the analysis of languages and practical applications. Particularly close to the interests of AI is the to study binary aggregation procedures, inspired by research problem of social choice in combinatorial domains (Chevaleyre in AI. As long as we do not know the intended application of et al., 2008), where the space of alternatives the individuals the model, there is no appropriate set of axioms to concentrate have to choose from has a combinatorial structure. Instead, we prove characterisation results concerning one Definition 1.

The counterfactual regret minimization (CFR) algorithm is state-of-the-art for computing strategies in large games and other sequential decision-making problems. Little is known, however, about CFR in games with more than 2 players. This extended abstract outlines research towards a better understanding of CFR in multiplayer games and new procedures for computing even stronger multiplayer strategies. We summarize work already completed that investigates techniques for creating "expert" strategies for playing smaller sub-games, and work that proves CFR avoids classes of undesirable strategies. In addition, we provide an outline of our future research direction. Our goals are to apply regret minimization to the problem of playing multiple games simultaneously, and augment CFR to achieve effective on-line opponent modelling of multiple opponents. The objective of this research is to build a world-class computer poker player for multiplayer Limit Texas Hold'em.