Almost all approaches to model-based diagnosis presume that the system being diagnosed behaves non-intermittently and analyze behavior over a small number (often only one) of time instants.  In this paper we show how existing approaches to model-based diagnosis can be extended to diagnose intermittent failures as they manifest themselves over time. In addition, we show where to insert probe points to best distinguish among the intermittent faults those that best explain the symptoms and isolate the fault in minimum expected cost.

We present an approach to semi-supervised learning based on an exponential family characterization. Our approach generalizes previous work on coupled priors for hybrid generative/discriminative models. Our model is more flexible and natural than previous approaches. Experimental results on several data sets show that our approach also performs better in practice. 

In many scenarios, quickly solving a relatively small search problem with an arbitrary start and arbitrary goal state is important (e.g., GPS navigation). In order to speed this process, we introduce a new class of memory-based heuristics, called true distance heuristics, that store true distances between some pairs of states in the original state space can be used for a heuristic between any pair of states. We provide a number of techniques for using and improving true distance heuristics such that most of the benefits of the all-pairs shortest-path computation can be gained with less than 1% of the memory. Experimental results on a number of domains show a 6-14 fold improvement in search speed compared to traditional heuristics.

P. This compilation, however, is linear in the number of possible initial states that is exponential in the number of fluents. The problem of planning in the presence of sensing We show nonetheless that even in such cases, a sound, has been addressed in recent years as a nondeterministic complete, and polynomial translation X(P) is possible, provided search problem in belief space. In this that the problem P has bounded contingent width, and work, we use ideas advanced recently for compiling show that the contingent width of almost all existing benchmarks conformant problems into classical ones for introducing is 1; a result that parallels the one reported by Palacios a different approach where contingent problems and Geffner for conformant planning. We then show how the P are mapped into non-deterministic problems non-deterministic but fully observable problem X(P) can be X(P) in state space.

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.

We determine the implicit assumptions and the structure of the Single and Double Cross Calculi within Euclidean geometry, and use these results to guide the construction of analogous calculi in mereogeometry.  The systems thus obtained have strong semantic and deductive similarities with the Euclidean-based Cross Calculi although they rely on a different geometry. This fact suggests that putting too much emphasis on usual classification of qualitative spaces may hide important commonalities among spaces living in different classes.

We propose a novel approach to context sensitive semantic smoothing by making use of an intermediate, "semantically light" representation for sentences, called Semantically Relatable Sequences (SRS). SRSs of a sentence are tuples of words appearing in the semantic graph of the sentence as linked nodes depicting dependency relations. In contrast to patterns based on consecutive words, SRSs make use of groupings of non-consecutive but semantically related words. Our experiments on TREC AP89 collection show that the mixture model of SRS translation model and Two Stage Language Model (TSLM) of Lafferty and Zhai achieves MAP scores better than the mixture model of MultiWord Expression (MWE) translation model and TSLM. Furthermore, a system, which for each test query selects either the SRS or the MWE mixture model based on better query MAP score, shows significant improvements over the individual mixture models.

Conflict driven clause learning, one of the most important component of modern SAT solvers, is also recognized as very important in parallel SAT solving. Indeed, it allows clause sharing between multiple processing units working on related (sub-)problems. However, without limitation, sharing clauses might lead to an exponential blow up in communication or to the sharing of irrelevant clauses. This paper, proposes two innovative policies to dynamically adjust the size of shared clauses between any pair of processing units. The first approach controls the overall number of exchanged clauses whereas the second additionally exploits the relevance quality of shared clauses. Experimental results show important improvements of the state-of the-art parallel SAT solver.

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.

In this paper, we describe an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilistic reasoning. Our specific focus is a class of oversubscribed scheduling problems where the goal is to maximize the reward earned by a team of agents in a distributed execution environment.  There is uncertainty in both the duration and  outcomes of executed activities. To ensure scalability, our solution approach takes as its starting point an initial deterministic schedule for the agents, computed using expected duration reasoning. This initial agent schedule is probabilistically analyzed to find likely points of failure, and then selectively strengthened based on this analysis. For each scheduled activity, the probability of failing and the impact that failure would have on the schedule's overall reward are calculated and used to focus schedule strengthening actions. Such actions generally entail fundamental trade-offs; for example, modifications that increase the certainty that a high-reward activity succeeds may decrease the schedule slack available to accommodate uncertainty during execution. We describe a principled approach to handling these trade-offs based on the schedule's "expected reward," using it as a metric to ensure that all schedule modifications are ultimately beneficial. Finally, we present experimental results obtained using a multi-agent simulation environment, which confirm that executing schedules strengthened in this way result in significantly higher rewards than are achieved by executing the corresponding initial schedules.