Differentiation is an important inference method in Bayesian networks and intervention is a basic notion in causal Bayesian networks. In this paper, we reveal the connection between differentiation and intervention in Bayesian networks. We first encode an intervention as changing a conditional probabilistic table into a partial intervention table. We next introduce a jointree algorithm to compute the full atomic interventions of all nodes with respect to evidence in a Bayesian network. We further discover that an intervention has differential semantics if the intervention variables can reach the evidence in Bayesian networks and the output of the state-of-the-art algorithm is not the differentiation but the intervention of a Bayesian network if the differential nodes cannot reach any one of the evidence nodes. Finally, we present experimental results to demonstrate the efficiency of our algorithm to infer the causal effect in Bayesian networks.

Probabilistic systems are an important theme in AI domain. As the specification language, PCTL is the most frequently used logic for reasoning about probabilistic properties. In this paper, we present a natural and succinct probabilistic extension of Mu-calculus, another prominent logic in the concurrency theory. We study the relationship with PCTL. Surprisingly, the expressiveness is highly orthogonal with PCTL. The proposed logic captures some useful properties which cannot be expressed in PCTL. We investigate the model checking and satisfiability problem, and show that the model checking problem is in UP and co-UP, and the satisfiability checking can be decided via reducing into solving parity games. This is in contrast to PCTL as well, whose satisfiability checking is still an open problem.

Marginal MAP is known to be a difficult task for graphical models, particularly because the evaluation of each MAP assignment involves a conditional likelihood computation. In order to minimize the number of likelihood evaluations, we focus in this paper on best-first search strategies for exploring the space of partial MAP assignments. We analyze the potential relative benefits of several best-first search algorithms and demonstrate their effectiveness against recent branch and bound schemes through extensive empirical evaluations. Our results show that best-first search improves significantly over existing depth-first approaches, in many cases by several orders of magnitude, especially when guided by relatively weak heuristics.

When Golog programs are used to control agents' behaviour in a high-level manner, their partial correctness naturally becomes an important concern. In this paper we propose a sound but incomplete method for automatic verification of partial correctness of Golog programs.  We introduce the notion of extended regression, which reduces partial correctness of Golog programs to first-order entailment problems.  During the process loop invariants are automatically discovered by heuristic methods.  We propose progression of small models wrt Golog programs, which are used to filter out too strong heuristic candidates.  In this way we combine the methods of static and dynamic analysis from the software engineering community.  Furthermore, our method can also be adapted to verify state constraints.  Experiments show that our method can not only handle sequential and nested loops uniformly in a reasonable among of time, but also be used to discover succinct and comprehensible loop invariants and state constraints.

Monte Carlo Tree Search (MCTS) is the state of the art algorithm for many games including the game of Go and General Game Playing (GGP). The standard algorithm for MCTS is Upper Confidence bounds applied to Trees (UCT). For games such as Go a big improvement over UCT is the Rapid Action Value Estimation (RAVE) heuristic. We propose to generalize the RAVE heuristic so as to have more accurate estimates near the leaves. We test the resulting algorithm named GRAVE for Atarigo, Knighthrough, Domineering and Go.

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems — that are in general NP-hard — efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on a selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques based on features of the decomposition rather than on the actual problem instance. We report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

Automatically identifying the research areas of academic/industry researchers is an important task for building expertise organizations or search systems. In general, this task can be viewed as text classification that generates a set of research areas given the expertise of a researcher like documents of publications. However, this task is challenging because the evidence of a research area may only exist in a few documents instead of all documents. Moreover, the research areas are often organized in a hierarchy, which limits the effectiveness of existing text categorization methods. This paper proposes a novel approach, Multi-instance Learning of Hierarchical Multi-label Classification Model (MIHML) for the task, which effectively identifies multiple research areas in a hierarchy from individual documents within the profile of a researcher. An Expectation-Maximization (EM) optimization algorithm is designed to learn the model parameters. Extensive experiments have been conducted to demonstrate the superior performance of proposed research with a real world application.

Given an over-constrained system, a Maximal Satisfiable Subset (MSS) denotes a maximal set of constraints that are consistent. A Minimal Correction Subset (MCS, or co-MSS) is the complement of an MSS. MSSes/MCSes find a growing range of practical applications, including optimization, configuration and diagnosis. A number of MCS extraction algorithms have been proposed in recent years, enabling very significant performance gains. This paper builds on earlier work and proposes a finer-grained view of the MCS extraction problem, one that reasons in terms of literals instead of clauses. This view is inspired by the relationship between MCSes and backbones of propositional formulas, which is further investigated, and allows for devising a novel algorithm. Also, the paper develops a number of techniques to approximate (weighted partial) MaxSAT by a selective enumeration of MCSes. Empirical results show substantial improvements over the state of the art in MCS extraction and indicate that MCS-based MaxSAT approximation is very effective in practice.

Inverse optimal control (IOC) is a powerful approach for learning robotic controllers from demonstration that estimates a cost function which rationalizes demonstrated control trajectories. Unfortunately, its applicability is difficult in settings where optimal control can only be solved approximately. While local IOC approaches have been shown to successfully learn cost functions in such settings, they rely on the availability of good reference trajectories, which might not be available at test time. We address the problem of using IOC in these computationally challenging control tasks by using a graph-based discretization of the trajectory space. Our approach projects continuous demonstrations onto this discrete graph, where a cost function can be tractably learned via IOC. Discrete control trajectories from the graph are then projected back to the original space and locally optimized using the learned cost function. We demonstrate the effectiveness of the approach with experiments conducted on two 7-degree of freedom robotic arms.

Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences. Second, we generate K-shortest paths from the sentences in each cluster using a word-graph structure. Finally, we select sentences from the set of shortest paths generated from all the clusters employing a novel integer linear programming (ILP) model with the objective of maximizing information content and readability of the final summary. Our ILP model represents the shortest paths as binary variables and considers the length of the path, information score and linguistic quality score in the objective function. Experimental results on the DUC 2004 and 2005 multi-document summarization datasets show that our proposed approach outperforms all the baselines and state-of-the-art extractive summarizers as measured by the ROUGE scores. Our method also outperforms a recent abstractive summarization technique. In manual evaluation, our approach also achieves promising results on informativeness and readability.