Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently, without the need of a plan library, using slightly modified planning algorithms. In this work, we extend this approach to the more general problem of probabilistic plan recognition where a probability distribution over the set of goals is sought under the assumptions that actions have deterministic effects and both agent and observer have complete information about the initial state. We show that this problem can be solved efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal probabilities, are computed by means of two calls to a classical planner that no longer has to be modified in any way. A number of examples is considered to illustrate the quality, flexibility, and scalability of the approach.

We consider settings in which voters vote in sequence, each voter knows the votes of the earlier voters and the preferences of the later voters, and voters are strategic. This can be modeled as an extensive-form game of perfect information, which we call a Stackelberg voting game. We first propose a dynamic-programming algorithm for finding the backward-induction outcome for any Stackelberg voting game when the rule is anonymous; this algorithm is efficient if the number of alternatives is no more than a constant. We show how to use compilation functions to further reduce the time and space requirements. Our main theoretical results are paradoxes for the backward-induction outcomes of Stackelberg voting games. We show that for any n ≥ 5 and any voting rule that satisfies nonimposition and with a low domination index, there exists a profile consisting of n voters, such that the backward-induction outcome is ranked somewhere in the bottom two positions in almost every voter’s preferences. Moreover, this outcome loses all but one of its pairwise elections. Furthermore, we show that many common voting rules have a very low (= 1) domination index, including all majority-consistent voting rules. For the plurality and nomination rules, we show even stronger paradoxes. Finally, using our dynamic-programming algorithm, we run simulations to compare the backward-induction outcome of the Stackelberg voting game to the winner when voters vote truthfully, for the plurality and veto rules. Surprisingly, our experimental results suggest that on average, more voters prefer the backward-induction outcome.

The problem of dividing a sequence of values into segments occurs in database systems, information retrieval, and knowledge management. The challenge is to select a finite number of boundaries for the segments so as to optimize an objective error function defined over those segments. Although this optimization problem can be solved in polynomial time, the algorithm which achieves the minimum error does not scale well, hence it is not practical for applications with massive data sets. There is considerable research with numerous approximation and heuristic algorithms. Still, none of those approaches has resolved the quality-efficiency tradeoff in a satisfactory manner. In (Halim, Karras, and Yap 2009), we obtain near linear time algorithms which achieve both the desired scalability and near-optimal quality, thus dominating earlier approaches. In this paper, we show how two ideas from artificial intelligence, an efficient local search and recombination of multiple solutions reminiscent of genetic algorithms, are combined in a novel way to obtain state of the art histogram construction algorithms.

Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster obeys Gaussian distribution and the task of clustering is to group observations into different components through estimating each cluster's own parameters. The Expectation-Maximization algorithm is always involved in such estimation problem. However, many previous studies have shown naturally occurring data may reside on or close to an underlying submanifold. In this paper, we consider the case where the probability distribution is supported on a submanifold of the ambient space. We take into account the smoothness of the conditional probability distribution along the geodesics of data manifold. That is, if two observations are close in intrinsic geometry, their distributions over different Gaussian components are similar. Simply speaking, we introduce a novel method based on manifold structure for data clustering, called Locally Consistent Gaussian Mixture Model (LCGMM). Specifically, we construct a nearest neighbor graph and adopt Kullback-Leibler Divergence as the distance measurement to regularize the objective function of GMM. Experiments on several data sets demonstrate the effectiveness of such regularization.

Many problems in AI, including planning, logical reasoning and probabilistic inference, have been shown to reduce to (weighted) constraint satisfaction. While there are a number of approaches for solving such problems, the recent gains in efficiency of the satisfiability approach have made SAT solvers a popular choice. Modern propositional SAT solvers are efficient for a wide variety of problems. However, particularly in the case of spatial reasoning, conversion to propositional SAT can sometimes result in a large number of variables and/or clauses. Moreover, spatial reasoning problems can often be more efficiently solved if the agent is able to exploit the geometric nature of space to make better choices during search and backtracking. The result of these two drawbacks — larger problem sizes and inefficient search — is that even simple spatial constraint problems are often intractable in the SAT approach. In this paper we propose a spatial reasoning system that provides significant performance improvements in constraint satisfaction problems involving spatial predicates. The key to our approach is to integrate a diagrammatic representation with a DPLL-based backtracking algorithm that is specialized for spatial reasoning. The resulting integrated system can be applied to larger and more complex problems than current approaches and can be adopted to improve performance in a variety of problems ranging from planning to probabilistic inference

We present the design and analysis of an approximately incentive-compatible combinatorial auction. In just a single run, the auction is able to extract enough value information from bidders to compute approximate truth-inducing payments. This stands in contrast to current auction designs that need to repeat the allocation computation as many times as there are bidders to achieve incentive compatibility. The auction is formulated as a kernel method, which allows for flexibility in choosing the price structure via a kernel function. Our main result characterizes the extent to which our auction is incentive-compatible in terms of the complexity of the chosen kernel function. Our analysis of the auction's properties is based on novel insights connecting the notion of stability in statistical learning theory to that of universal competitive equilibrium in the auction literature.

In the form of topic discussions, users interact with each other to share knowledge and exchange information in online forums. Modeling the evolution of topic discussion reveals how information propagates on Internet and can thus help understand sociological phenomena and improve the performance of applications such as recommendation systems. In this paper, we argue that a user’s participation in topic discussions is motivated by either her friends or her own preferences. Inspired by the theory of information flow, we propose dynamic topic discussion models by mining influential relationships between users and individual preferences. Reply relations of users are exploited to construct the fundamental influential social network. The property of discussed topics and time lapse factor are also considered in our modeling. Furthermore, we propose a novel measure called ParticipationRank to rank users according to how important they are in the social network and to what extent they prefer to participate in the discussion of a certain topic. The experiments show our model can simulate the evolution of topic discussions well and predict the tendency of user’s participation accurately.

Human experts in scientific fields routinely work with evidence that is noisy and untrustworthy, heuristics that are unproven, and possible conclusions that are contradictory. We present a fully implemented AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses two-dimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts.

On the surface, bidirectional search (BDS) is an attractive idea with the potential for significant asymptotic reductions in search effort. However, the results in practice often fall far short of expectations. We introduce a new bidirectional search algorithm, Single-Frontier Bidirectional Searc (SFBDS). Unlike traditional BDS which keeps two frontiers, SFBDS uses a single frontier. Each node in the tree can be seen as an independent task of finding the shortest path between the current start and current goal. At a particular node we can decide to search from start to goal or from goal to start, choosing the direction with the highest potential for minimizing the total work done. Theoretical results give insights as to when this approach will work and experimental data validates the algorithm for a broad range of domains.

In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists’ standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists’ standpoints.