Abductive inference is an important AI reasoning technique to find explanations of observations, and has recently been applied to scientific discovery.  To find best hypotheses among many logically possible hypotheses, we need to evaluate hypotheses obtained from the process of hypothesis generation.  We propose an abductive inference architecture combined with an EM algorithm working on binary decision diagrams (BDDs).  This work opens a way of applying BDDs to compress multiple hypotheses and to select most probable ones from them.  An implemented system has been applied to inference of inhibition in metabolic pathways in the domain of systems biology.

Building and using agent-based models is often impractical, in part due to the cost of including expensive subject matter experts (SMEs) in the development process. In this paper, we describe a method for "bootstrapping" model building to lower the cost of overall model development. The models we are interested in here capture dynamic phenomena related to international and subnational conflict. The method of acquiring these models begins with event data drawn from news reports about a conflict region, and infers model characteristics particular to a conflict modeling framework called the Power Structure Toolkit (PSTK). We describe the toolkit and how it has been used prior to this work. We then describe the current problem of modeling conflict and the empirical data available to learn models, and extensions to the PSTK for model generation from this data. We also describe a formative evaluation of the system that compares the performance and costs of models built entirely by an SME against models built with an SME aided by the automated model generation process. Early results indicate at least equivalent prediction rates with significant savings in model generation costs.

Given an imagebase with tagged images, four types of tasks an be executed, i.e., content-based image retrieval, image annotation, text-based image retrieval, and query expansion. For any of these tasks the similarity on the concerned type of objects is essential. In this paper, we propose a framework to tackle these four tasks from a unified view. The essence of the framework is to estimate similarities by exploiting the interactions between objects of different modality. Experiments show that the proposed method can improve similarity estimation, and based on the improved similarity estimation, some simple methods can achieve better performances than some state-of-the-art techniques.

Location  plays a very important role in the retail business due to its huge and long-term investment. In this paper, we propose a novel semi-supervised regression model for evaluating convenience store location based on spatial data analysis. First, the input features for each convenience store can be extracted by analyzing the elements around it based on a geographic information system, and the turnover is used to evaluate its performance. Second, considering the practical application scenario, a manifold regularization model with one semi-supervised performance information constraint is provided. The promising experimental results in the real-world dataset demonstrate the effectiveness of the proposed approach  in performance prediction of  certain candidate locations for new convenience store opening.

In machine learning problems, labeled data are often in short supply. One of the feasible solution for this problem is transfer learning. It can make use of the labeled data from other domain to discriminate those unlabeled data in the target domain. In this paper, we propose a transfer learning framework based on similarity matrix approximation to tackle such problems. Two practical algorithms are proposed, which are the label propagation and the similarity propagation. In these methods, we build a hybrid graph based on all available data. Then the information is transferred cross domains through alternatively constructing the similarity matrix for different part of the graph. Among all related methods, similarity propagation approach can make maximum use of all available similarity information across domains. This leads to more efficient transfer and better learning result. The experiment on real world text mining applications demonstrates the promise and effectiveness of our algorithms.

This paper proposes a novel framework for image retrieval. The retrieval is treated as searching for an ordered cycle in an image database. The optimal cycle can be found by minimizing the geometric manifold entropy of images. The minimization is solved by the proposed method, fast active tabu search. Experimental results demonstrate the framework for image retrieval is feasible and quite promising.

Detecting abnormal activities from sensor readings is an important research problem in activity recognition. A number of different algorithms have been proposed in the past to tackle this problem. Many of the previous state-based approaches suffer from the problem of failing to decide the appropriate number of states, which are difficult to find through a trial and-error approach, in real-world applications. In this paper, we propose an accurate and flexible framework for abnormal activity recognition from sensor readings that involves less human tuning of model parameters. Our approach first applies a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), which supports an infinite number of states, to automatically find an appropriate number of states. We incorporate a Fisher Kernel into the One-Class Support Vector Machine (OCSVM) model to filter out the activities that are likely to be normal. Finally, we derive an abnormal activity model from the normal activity models to reduce false positive rate in an unsupervised manner. Our main contribution is that our proposed HDP-HMM models can decide the appropriate number of states automatically, and that by incorporating a Fisher Kernel into the OCSVM model, we can combine the advantages from generative model and discriminative model. We demonstrate the effectiveness of our approach by using several real-world datasets to test our algorithm’s performance.

Understanding the computational complexity of manipulation in elections is arguably the most central agenda in Computational Social Choice. One of the influential variations of the the problem involves a coalition of manipulators trying to make a favorite candidate win the election. Although the complexity of the problem is well-studied under the assumption that the voters are weighted, there were very few successful attempts to abandon this strong assumption. In this paper, we study the complexity of the unweighted coalitional manipulation problem (UCM) under several prominent voting rules. Our main result is that UCM is NP-complete under the maximin rule; this resolves an enigmatic open question. We then show that UCM is NP-complete under the ranked pairs rule, even with respect to a single manipulator. Furthermore, we provide an extreme hardness-of-approximation result for an optimization version of UCM under ranked pairs. Finally, we show that UCM under the Bucklin rule is in P.

Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization. This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and the cross-document sentence relationships as two separate modalities (graphs). Three different fusion schemes, namely linear form, sequential form and score combination form, are exploited in the algorithm. Experimental results on the DUC benchmark datasets demonstrate the effectiveness of the proposed multi-modality learning algorithms with all the three fusion schemes.

In many real world planning domains, some observation information is optional and useless to the execution of a plan; on the other hand, information acquisition may require some kind of cost. The problem of observation reduction for strong plans has been addressed in the literature. However, observation reduction for plans with contexts (which are more general and useful than strong plans in robotics) is still a open problem. In this paper, we present an attempt to solve the problem. Our first contribution is the definition of structured plans, which can encode sequential, conditional and iterative behaviors, and is expressive enough for dealing with incomplete observation information and internal states of the agent. A second contribution is an observation reduction algorithm for plans with contexts, which can transform a plan with contexts into a structured plan that only branches on necessary observation information.