Artificial societies — distributed systems of autonomous agents — are becoming increasingly important in e-commerce. Agents base their decisions on trust and reputation in ways analogous to human societies. Many different definitions for trust and reputation have been proposed that incorporate many sources of information; however, system designs have tended to focus much of their attention on direct interactions. Furthermore, trust updating schemes for direct interactions have tended to uncouple updates for positive and negative feedback. Consequently, behaviour in which cycles of positive feedback followed by a single negative feedback results in untrustworthy agents remaining undetected. This con-man style of behaviour is formally described and desirable characteristics of con-resistant trust schemes proposed. A con-resistant scheme is proposed and compared with FIRE, Regret and Yu and Singh's model. Simulation experiments demonstrate the utility of the con-resistant scheme.

This can increase the size of the Treewidth is a fundamental property of a graph with significant search exponentially. We explore two techniques implications for several areas of artificial intelligence that prevent this: duplicate detection and duplicate research. A reason for focusing on treewidth is that a natural avoidance. We illustrate these techniques on search space for it uses a maximum edge cost function. As the treewidth problem, a combinatorial optimization we discuss in a later section, in an iterative-deepening search problem with applications to a variety of research on a problem with a maximum edge cost function, every duplicate areas. The bottleneck for previous treewidth node can be discarded. This makes these problems algorithms is a large memory requirement. We develop well-suited for studying duplicate elimination techniques.

This paper deals with the relationship between intelligent behaviour, on the   one hand, and the mental qualities needed to produce it, on the other.  We   consider two well-known opposing positions on this issue: one due to Alan   Turing and one due to John Searle (via the Chinese Room).  In particular, we   argue against Searle, showing that his answer to the so-called System Reply   does not work.  The argument takes a novel form:   we shift the debate to a different and more plausible room where the   required conversational behaviour is much easier to characterize and to   analyze.  Despite being much simpler than the Chinese Room, we show that    the  behaviour there is still complex enough that it cannot be produced without   appropriate mental qualities.

Search methods based on Monte-Carlo simulation have recently led to breakthrough performance improvements in difficult game-playing domains such as Go and General Game Playing. Monte-Carlo Random Walk (MRW) planning applies Monte-Carlo ideas to deterministic classical planning. In the forward chaining planner Arvand, Monte-Carlo random walks are used to explore the local neighborhood of a search state for action selection. In contrast to the stochastic local search approach used in the recent planner Identidem, random walks yield a larger and unbiased sample of the search neighborhood, and require state evaluations only at the endpoints of each walk. On IPC-4 competition problems, the performance of Arvand is competitive with state of the art systems.

Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares policy iteration (LSPI) framework allows us to employ statistical active learning methods for linear regression. Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high. We demonstrate the usefulness of the proposed method, named active policy iteration (API), through simulations with a batting robot.

This is also a key issue in formalizing deals with incomplete information, preferences, negotiation, which seems to prefer argumentationbased and changing goals. We assume that each negotiation [Rahwan et al., 2003]. Recent proposals agent is equipped with a knowledge base for negotiation on formalizing negotiation (see, e.g., [Amgoud et al., 2006; which consists of a CRprogram, a set of possible Kakas and Moraitis, 2006; Rahwan et al., 2003]) seem to be assumptions, and a set of ordered goals.

Bootstrap Voting Experts (BVE) is an extension to the Voting Experts algorithm for unsupervised chunking of sequences. BVE generates a series of segmentations, each of which incorporates knowledge gained from the previous segmentation. We show that this method of bootstrapping improves the performance of Voting Experts in a variety of unsupervised word segmentation scenarios, and generally improves both precision and recall of the algorithm. We also show that Minimum Description Length (MDL) can be used to choose nearly optimal parameters for Voting Experts in an unsupervised manner.

Consider the setting where a panel of judges is repeatedly asked to (partially) rank sets of objects according to given criteria, and assume that the judges' expertise depends on the objects' domain.  Learning to aggregate their rankings with the goal of producing a better joint ranking is a fundamental problem in many areas of Information Retrieval and Natural Language Processing, amongst others.  However, supervised ranking data is generally difficult to obtain, especially if coming from multiple domains.  Therefore, we propose a framework for learning to aggregate votes of constituent rankers with domain specific expertise without supervision.  We apply the learning framework to the settings of aggregating full rankings and aggregating top-k lists, demonstrating significant improvements over a domain-agnostic baseline in both cases.

In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G ∗ of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goal G belongs to G ∗ if there is an action sequence π that is an optimal plan for both the goal G and the goal G extended with extra goals representing the observations. Exploiting this result, we show how the set G ∗ can be computed exactly and approximately by minor modifications of existing optimal and suboptimal planning algorithms, and existing polynomial heuristics. Experiments over several domains show that the suboptimal planning algorithms and the polynomial heuristics provide good approximations of the optimal goal set G ∗ while scaling up as well as state-of-the-art planning algorithms and heuristics.

The number partitioning problem is to divide a given set of integers into a collection of subsets, so that the sum of the numbers in each subset are as nearly equal as possible.  While a very efficient algorithm exists for optimal two-way partitioning, it is not nearly as effective for multi-way partitioning. We develop two new linear-space algorithms for multi-way partitioning, and demonstrate their performance on three, four, and five-way partitioning.  In each case, our algorithms outperform the previous state of the art by orders of magnitude, in one case by over six orders of magnitude.  Empirical analysis of the running times of our algorithms strongly suggest that their asymptotic growth is less than that of previous algorithms.  The key insight behind both our new algorithms is that if an optimal k-way partition includes a particular subset, then optimally partitioning the numbers not in that set k-1 ways results in an optimal k-way partition.