Mechanisms (especially on the Internet) have begun allowing people or organizations to express richer preferences in order to provide for greater levels of overall satisfaction.  In this paper, we develop an operational methodology for quantifying the expected gains in economic efficiency associated with different forms of expressiveness.  We begin by proving that the sponsored search mechanism (GSP) used by Google, Yahoo!, MSN, etc. can be arbitrarily inefficient. We then experimentally compare its efficiency to a slightly more expressive variant (PGSP), which solicits an extra bid for a premium class of positions. We generate random preference distributions based on published industry knowledge.  We determine ideal strategies for the agents using a custom tree search technique, and we also benchmark using straightforward heuristic bidding strategies. The GSP's efficiency loss is greatest in the practical case where some advertisers ("brand advertisers") prefer top positions while others ("value advertisers") prefer middle positions, and that loss can be dramatic. It is also worst when agents have small profit margins. While the PGSP is only slightly more expressive (and thus not much more cumbersome), it removes almost all of the efficiency loss in all of the settings we study.

Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive compatible, direct mechanisms that are efficient (i.e., maximise social utility) and individually rational (i.e., agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will "always" successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications, where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of "trust". Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called "trust-based mechanisms", that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^5 possible allocations in 40 seconds).

Mining frequent sequential patterns from sequence databases has been a central research topic in data mining and various efficient mining sequential patterns algorithms have been proposed and studied. Recently, in many problem domains (e.g, program execution traces), a novel sequential pattern mining research, called mining repetitive gapped sequential patterns, has attracted the attention of many researchers, considering not only the repetition of sequential pattern in different sequences but also the repetition within a sequence is more meaningful than the general sequential pattern mining which only captures occurrences in different sequences. However, the number of repetitive gapped sequential patterns generated by even these closed mining algorithms may be too large to understand for users, especially when support threshold is low. In this paper, we propose and study the problem of compressing repetitive gapped sequential patterns. Inspired by the ideas of summarizing frequent itemsets, RPglobal, we develop an algorithm, CRGSgrow (Compressing Repetitive Gapped Sequential pattern grow), including an efficient pruning strategy, SyncScan, and an efficient representative pattern checking scheme, -dominate sequential pattern checking. The CRGSgrow is a two-step approach: in the first step, we obtain all closed repetitive sequential patterns as the candidate set of representative repetitive sequential patterns, and at the same time get the most of representative repetitive sequential patterns; in the second step, we only spend a little time in finding the remaining the representative patterns from the candidate set. An empirical study with both real and synthetic data sets clearly shows that the CRGSgrow has good performance.

Thanks to recent advances, AI Planning has become the underlying technique for several applications. Figuring prominently among these is automated Web Service Composition (WSC) at the "capability" level, where services are described in terms of preconditions and effects over ontological concepts. A key issue in addressing WSC as planning is that ontologies are not only formal vocabularies; they also axiomatize the possible relationships between concepts. Such axioms correspond to what has been termed "integrity constraints" in the actions and change literature, and applying a web service is essentially a belief update operation. The reasoning required for belief update is known to be harder than reasoning in the ontology itself. The support for belief update is severely limited in current planning tools. Our first contribution consists in identifying an interesting special case of WSC which is both significant and more tractable. The special case, which we term "forward effects", is characterized by the fact that every ramification of a web service application involves at least one new constant generated as output by the web service. We show that, in this setting, the reasoning required for belief update simplifies to standard reasoning in the ontology itself. This relates to, and extends, current notions of "message-based" WSC, where the need for belief update is removed by a strong (often implicit or informal) assumption of "locality" of the individual messages. We clarify the computational properties of the forward effects case, and point out a strong relation to standard notions of planning under uncertainty, suggesting that effective tools for the latter can be successfully adapted to address the former. Furthermore, we identify a significant sub-case, named "strictly forward effects", where an actual compilation into planning under uncertainty exists. This enables us to exploit off-the-shelf planning tools to solve message-based WSC in a general form that involves powerful ontologies, and requires reasoning about partial matches between concepts. We provide empirical evidence that this approach may be quite effective, using Conformant-FF as the underlying planner.

Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information. In this paper, we experiment with one empirical method and two unsupervised statistical machine learning techniques: K-means and Expectation Maximization (EM), for computing relative importance of the sentences. We compare the results of these approaches. Our experiments show that the empirical approach outperforms the other two techniques and EM performs better than K-means. However, the performance of these approaches depends entirely on the feature set used and the weighting of these features. In order to measure the importance and relevance to the user query we extract different kinds of features (i.e. lexical, lexical semantic, cosine similarity, basic element, tree kernel based syntactic and shallow-semantic) for each of the document sentences. We use a local search technique to learn the weights of the features. To the best of our knowledge, no study has used tree kernel functions to encode syntactic/semantic information for more complex tasks such as computing the relatedness between the query sentences and the document sentences in order to generate query-focused summaries (or answers to complex questions). For each of our methods of generating summaries (i.e. empirical, K-means and EM) we show the effects of syntactic and shallow-semantic features over the bag-of-words (BOW) features.

This paper proposes a new approach to improve domain independent heuristic state space search planners for classical planning by tuning the search heuristics using macro actions of length two extracted from sample plans. This idea is implemented in the planner AltAlt and the new planner Macro-AltAlt is tested on the domains introduced for the learning track of the International Planning Competition (IPC-2008). The performance of Macro-AltAlt measured by the length of the plan found and the number of states explored to find the plan is compared with that of AltAlt.

In this paper an application of the metaheuristic Ant Colony Optimization to optimal planning is presented. It is well known that finding out optimal solutions to planning problem is a very hard computational problem. Approximate methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good solutions, often close to optimal ones. Since one of the most performing stochastic method for combinatorial optimization is ACO, we have decided to use this technique to design an algorithm which optimizes plan length in propositional planning. This algorithm has been implemented and some empirical evaluations have been performed. The results obtained are encouraging and show the feasibility of this approach.

The field of artificial intelligence needs to attract new researchers to the field to continue current explorations and look for novel approaches to tomorrow's problems. One approach involves providing students with learning tools that excite their imagination and help them obtain an appreciation for what artificial intelligence can do. The tools described here are used in an undergraduate course at Sam Houston State University. They include heuristic-driven search in a potential game's terrain map, reinforcement learning in a tank battle game, and game tree search techniques in tic-tac-toe.

In this paper, we examine Robot Defense, a computer game that serves as a pedagogical platform for students to explore methods typically covered in an Introductory Artificial Intelligence course. Robot Defense is the synergistic outcome of two NSF funded Course, Curriculum, and Laboratory Improvement (CCLI) projects and was first presented in (Wallace, Russell and Markov 2008). The primary contribution of this paper is to discuss the implementation of the Robot Defense platform and the outcome of its first use in the classroom.

Many important tasks can be cast as weighted relational satisfiability problems.  Propositionalizing relational theories and making inferences with them using SAT algorithms has proven effective in many cases.  However, these approaches require that all objects in a domain be known in advance.  Many domains, from language understanding to machine vision, involve reasoning about objects that are not known beforehand.  Theories with unknown objects can require models with infinite objects in their domain and thus lead to propositionalized SAT theories that existing algorithms cannot deal with.  To address these problems, we characterize a class of relational generative weighted satisfiability theories (GenSAT) over potentially infinite domains and propose an algorithm, GenDPLL, for finding models of these theories.  We introduce the notion of a relevant model and an increasing cost theory to identify conditions under which GenDPLL is complete, even when a theory has infinite models.