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.

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.

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.

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.

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.

Object recognition using image-set or video sequence as input tends to be more robust since image-set or video sequence provides much more information than single snap-shot about the variability in the appearance of the target subject. Constrained Mutual Subspace Method (CMSM) is one of the state-of-the-art algorithms for imageset based object recognition by first projecting the image-set patterns onto the so-called generalized difference subspace then classifying based on the principal angle based mutual subspace distance. By treating the subspace bases for each image-set patterns as basic elements in the grassmann manifold, this paper presents a framework for robust image-set based recognition by CMSM based ensemble learning in a boosting way. The proposed Boosting Constrained Mutual Subspace Method(BCMSM) improves the original CMSM in the following ways: a) The proposed BCMSM algorithm is insensitive to the dimension of the generalized differnce subspace while the performance of the original CMSM algorithm is quite dependent on the dimension and the selecting of optimum choice is quite empirical and case-dependent; b) By taking advantage of both boosting and CMSM techniques, the generalization ability is improved and much higher classification performance can be achieved. Extensive experiments on real-life data sets (two face recognition tasks and one 3D object category classification task) show that the proposed method outperforms the previous state-of-the-art algorithms greatly in terms of classification accuracy.

The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring user-item rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.

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.