Tractability analysis in terms of the causal graphs of planning problems has emerged as an important area of research in recent years, leading to new methods for the derivation of domain-independent heuristics (Katz and Domshlak 2010). Here we continue this work, extending our knowledge of the frontier between tractable and NP-complete fragments. We close some gaps left in previous work, and introduce novel causal graph fragments that we call the hourglass and semifork, for which under certain additional assumptions optimal planning is in P. We show that relaxing any one of the restrictions required for this tractability leads to NP-complete problems. Our results are of both theoretical and practical interest, as these fragments can be used in existing frameworks to derive new abstraction heuristics. Before they can be used, however, a number of practical issues must be addressed. We discuss these issues and propose some solutions.

We introduce a linear distance relaxation of the n-team Traveling Tournament Problem (TTP), a simple yet powerful heuristic that temporarily "assumes"' the n teams are located on a straight line, thereby reducing the n ( n –1)/2 pairwise distance parameters to just n –1 variables. The modified problem then becomes easier to analyze, from which we determine an approximate solution for the actual instance on n teams. We present combinatorial techniques to solve the Linear Distance TTP (LD-TTP) for n = 4 and n = 6, without any use of computing, generating the complete set of optimal distances regardless of where the n teams are located. We show that there are only 295 non-isomorphic schedules that can be a solution to the 6-team LD-TTP, and demonstrate that in all previously-solved benchmark TTP instances on 6 teams, the distance-optimal schedule appears in this list of 295, even when the six teams are arranged in a circle or located in three-dimensional space. We then extend the LD-TTP to multiple rounds, and apply our theory to produce a nearly-optimal regular-season schedule for the Nippon Pro Baseball league in Japan. We conclude the paper by generalizing our theory to the n -team LD-TTP, producing a feasible schedule whose total distance is guaranteed to be no worse than 4/3 times the optimal solution.

It has been shown recently that the complexity of belief tracking in deterministic conformant and contingent planning is exponential in a width parameter that is often bounded and small. In this work, we introduce a new width notion that applies to non-deterministic conformant and contingent problems as well. We also develop a belief tracking algorithm for non-deterministic problems that is exponential in the problem width, analyze the width of non-deterministic benchmarks, compare the new notion to the previous one over deterministic problems, and present experimental results.

One of the natural approaches for selecting actions in very From this perspective, an algorithm like RTDP fails on two large state spaces is by performing a limited amount of grounds: first, RTDP does not appear to make best use of lookahead. In the contexts of discounted MDPs, Kearns, short time windows in large state spaces; second, and more Mansour, and Ng have shown that near to optimal actions importantly, RTDP can use admissible heuristics but not informed can be selected by considering a sampled lookahead tree that base policies. On the other hand, algorithms like Policy is sufficiently sparse, whose size depends on the discount Iteration (Howard 1971), deliver all of these features except factor and the suboptimality bound but not on the number of one: they are exhaustive, and thus even to get started, problem states (Kearns, Mansour, and Ng 1999). The UCT they need vectors with the size of the state space. At the algorithm (Kocsis and Szepesvári 2006) is a version of this same time, while there are non-exhaustive versions of (asynchronous) form of Monte Carlo planning, where the lookahead trees Value Iteration such as RTDP, there are no similar are not grown depth-first but'best-first', following a selection'focused' versions of Policy Iteration ensuring anytime optimality.

We introduce the Multiagent Disjunctive Temporal Problem (MaDTP), a new distributed formulation of the widely-adopted Disjunctive Temporal Problem (DTP) representation. An agent that generates a summary of all viable schedules, rather than a single schedule, can be more useful in dynamic environments. We show how a (Ma)DTP with the properties of minimality and decomposability provides a particularly efficacious solution space summary.However, in the multiagent case, these properties sacrifice an agent's strategic interests while incurring significant computational overhead. We introduce a new property called local decomposability that exploits loose-coupling between agents' problems, protects strategic interests, and supports typical queries. We provide and evaluate a new distributed algorithm that summarizes agents' solution spaces in significantly less time and space by using local, rather than full, decomposability.

The early classifications of the computational complexity of planning under various restrictions in STRIPS (Bylander) and SAS+ (Bäckström and Nebel) have influenced following research in planning in many ways. We go back and reanalyse their subclasses, but this time using the more modern tool of parameterized complexity analysis. This provides new results that together with the old results give a more detailed picture of the complexity landscape. We demonstrate separation results not possible with standard complexity theory, which contributes to explaining why certain cases of planning have seemed simpler in practice than theory has predicted. In particular, we show that certain restrictions of practical interest are tractable in the parameterized sense of the term, and that a simple heuristic is sufficient to make a well-known partial-order planner exploit this fact.

Initiated by TAC 2010, aspect-guided summaries not only address specific user need, but also ameliorate content-level coherence by using aspect information. This paper presents a full-fledged system composed of three modules: finding sentence-level textual aspects, modeling aspect-based coherence with an HMM model, and selecting and ordering sentences with aspect information to generate coherent summaries. The evaluation results on the TAC 2011 datasets show the superiority of aspect-guided summaries in terms of both information coverage and textual coherence.

Predicting entailment between two given texts is an important task upon which the performance of numerous NLP tasks depend on such as question answering, text summarization, and information extraction. The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose a method that jointly learns both (a) a set of non-linear transformation functions for those similarity measures and, (b) the optimal non-linear combination of those transformation functions to predict textual entailment. Our method consistently outperforms numerous baselines, reporting a micro-averaged F-score of 46.48 on the RTE- 7 benchmark dataset. The proposed method is ranked 2-nd among 33 entailment systems participated in RTE-7, demonstrating its competitiveness over numerous other entailment approaches. Although our method is statistically comparable to the current state-of-the-art, we require less external knowledge resources.

Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of expertise (e.g., labeling gene names from plain text requires corresponding domain knowledge); (2) ignored or omitted annotations due to carelessness or low confidence; (3) noisy annotations due to cheating or vandalism. To correct these mistakes, we present Sembler, a statistical model for ensembling crowd sequential labelings. Sembler considers three types of statistical information: (1) the majority agreement that proves the correctness of an annotation; (2) correct annotation that improves the credibility of the corresponding annotator; (3) correct annotation that enhances the correctness of other annotations which share similar linguistic or contextual features. We evaluate the proposed model on a real Twitter and a synthetical biological data set, and find that Sembler is particularly accurate when more than half of annotators make mistakes.

This paper describes an emotion-based approach to acquire sentiment similarity of word pairs with respect to their senses. Sentiment similarity indicates the similarity between two words from their underlying sentiments. Our approach is built on a model which maps from senses of words to vectors of twelve basic emotions. The emotional vectors are used to measure the sentiment similarity of word pairs. We show the utility of measuring sentiment similarity in two main natural language processing tasks, namely, indirect yes/no question answer pairs (IQAP) Inference and sentiment orientation (SO) prediction. Extensive experiments demonstrate that our approach can effectively capture the sentiment similarity of word pairs and utilize this information to address the above mentioned tasks.