When to make a decision is a key question in decision making problems characterized by uncertainty. In this paper we deal with decision making in environments where the information arrives dynamically. We address the tradeoff between waiting and stopping strategies. On the one hand, waiting to obtain more information reduces the uncertainty, but it comes with a cost. On the other hand, stopping and making a decision based on an expected utility, decreases the cost of waiting, but the decision is made based on uncertain information. In this paper, we prove that computing the optimal time to make a decision that guarantees the optimal utility is NP-hard. We propose a pessimistic approximation that guarantees an optimal decision when the recommendation is to wait. We empirically evaluate our algorithm and show that the quality of the decision is near-optimal and much faster than the optimal algorithm.

Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and vision applications. However, no coarse-to-fine inference or learning methods have been developed for general first-order probabilistic domains, where the potential gains are even higher. We present our Coarse-to-Fine Probabilistic Inference (CFPI) framework for general coarse-to-fine inference for first-order probabilistic models, which leverages a given or induced type hierarchy over objects in the domain. Starting by considering the inference problem at the coarsest type level, our approach performs inference at successively finer grains, pruning high- and low-probability atoms before refining. CFPI can be applied with any probabilistic inference method and can be used in both propositional and relational domains. CFPI provides theoretical guarantees on the errors incurred, and these guarantees can be tightened when CFPI is applied to specific inference algorithms. We also show how to learn parameters in a coarse-to-fine manner to maximize the efficiency of CFPI. We evaluate CFPI with the lifted belief propagation algorithm on social network link prediction and biomolecular event prediction tasks. These experiments show CFPI can greatly speed up inference without sacrificing accuracy.

With the recent inclusion of inter-league games to professional sports leagues, a natural question is to determine the "best possible" inter-league schedule that retains all of the league's scheduling constraints to ensure competitive balance and fairness, while minimizing the total travel distance for both economic and environmental efficiency. To answer that question, this paper introduces the Bipartite Traveling Tournament Problem (BTTP) , the inter-league extension of the well-studied Traveling Tournament Problem. We prove that the 2n -team BTTP is NP-complete, but for small values of n , a distance-optimal inter-league schedule can be generated from an algorithm based on minimum-weight 4-cycle-covers. We apply our algorithm to the 12-team Nippon Professional Baseball (NPB) league in Japan, creating an inter-league tournament that reduces total team travel by 16% compared to the actual schedule played by these teams during the 2010 NPB season. We also analyze the problem of inter-league scheduling for the 30-team National Basketball Association (NBA), and develop a tournament schedule whose total inter-league travel distance is just 3.8% higher than the trivial theoretical lower bound.  

Diversity-aware planning consists of generating multiple plans which, while solving the same problem, are dissimilar from one another. Quantitative plan diversity is domain-independent and does not require extensive knowledge-engineering effort, but can fail to reflect plan differences that are relevant to users. Qualitative plan diversity is based on domain-specific characteristics, thus being of greater practical value, but may require substantial knowledge engineering. We demonstrate a domain-independent diverse plan generation method that is based on customizable plan distance metrics and amenable to both quantitative and qualitative diversity. Qualitative plan diversity is obtained with minimal knowledge-engineering effort, using distance metrics which incorporate domain-specific content.

In this paper, we model the pair-wise similarities of a setof documents as a weighted network with a single cutoffparameter. Such a network can be thought of an ensemble of unweighted graphs, each consisting of edges withweights greater than the cutoff value. We look at this network ensemble as a complex system with a temperature parameter, and refer to it as a Latent Network. Ourexperiments on a number of datasets from two different domains show that certain properties of latent networks like clustering coefficient, average shortest path,and connected components exhibit patterns that are significantly divergent from randomized networks. We explain that these patterns reflect the network phase transition as well as the existence of a community structure in document collections. Using numerical analysis,we show that we can use the aforementioned networkproperties to predicts the clustering Normalized MutualInformation (NMI) with high correlation (rho > 0.9). Finally we show that our clustering method significantlyoutperforms other baseline methods (NMI > 0.5)

We present a method for dependency grammar induction that utilizes sparse annotations of semantic relations. This induction set-up is attractive because such annotations provide useful clues about the underlying syntactic structure, and they are readily available in many domains (e.g., info-boxes and HTML markup). Our method is based on the intuition that syntactic realizations of the same semantic predicate exhibit some degree of consistency. We incorporate this intuition in a directed graphical model that tightly links the syntactic and semantic structures. This design enables us to exploit syntactic regularities while still allowing for variations. Another strength of the model lies in its ability to capture non-local dependency relations. Our results demonstrate that even a small amount of semantic annotations greatly improves the accuracy of learned dependencies when tested on both in-domain and out-of-domain texts.

Semantic relatedness is the task of finding and quantifying Knowledge-based measures such as L&C (Leacock the strength of the semantic connections that exist between and Chodorow 1998), Lesk (Lesk 1986), Wu&Palmer (Wu textual units, be they word pairs, sentence pairs, or document and Palmer 1994), Resnik (Resnik 1995), J&C (Jiang and pairs. For instance, one may want to determine how Conrath 1997), H&S (Hirst and St Onge 1998), and many semantically related are car and automobile, ornoon and others, employ information extracted from manually constructed string. To make such a judgment, we rely on our accumulated lexical taxonomies like Wordnet (Fellbaum 1998), knowledge and experiences, and utilize our ability Roget (Jarmasz 2003), and Wiktionary (Zesch, Muller, and of conceptual thinking, abstraction, and generalization.

Most existing Question Answering (QA) systems adopt a type-and-generate approach to candidate generation that relies on a pre-defined domain ontology. This paper describes a type independent search and candidate generation paradigm for QA that leverages Wikipedia characteristics. This approach is particularly useful for adapting QA systems to domains where reliable answer type identification and type-based answer extraction are not available. We present a three-pronged search approach motivated by relations an answer-justifying title-oriented document may have with the question/answer pair. We further show how Wikipedia metadata such as anchor texts and redirects can be utilized to effectively extract candidate answers from search results without a type ontology. Our experimental results show that our strategies obtained high binary recall in both search and candidate generation on TREC questions, a domain that has mature answer type extraction technology, as well as on Jeopardy! questions, a domain without such technology. Our high-recall search and candidate generation approach has also led to high overall QA performance in Watson, our end-to-end system.

Recently the general Game Description Language (GDL) has been extended so as to cover arbitrary games with incomplete/imperfect information. Learning — without human intervention — to play such games poses a reasoning challenge for general game-playing systems that is much more intricate than in case of complete information games. Action formalisms like the Situation Calculus have been developed for precisely this purpose. In this paper we present a full embedding of the Game Description Language into the Situation Calculus (with Scherl and Levesque's knowledge fluent ). We formally prove that this provides a sound and complete reasoning method for players' knowledge about game states as well as about the knowledge of the other players.

Empirical findings indicate that humans draw infer- ences about spatial arrangements by constructing and manipulating mental models which are internal representations of objects and relations in spatial working memory. Central to the Mental Model Theory (MMT), is the assumption that the human reasoning process can be divided into three phases: (i) Mental model construction, (ii) model inspection, and (iii) model validation. The MMT can be formalized with respect to a computational model, connecting the reasoning process to operations on mental model representations. In this respect a computational model has been implemented in the cognitive architecture ACT-R capable of explaining human reasoning difficulty by the number of model operations. The presented ACT-R model allows simulation of psychological findings about spatial reasoning problems from a previous study that investigated conventional behavioral data such as response times and error rates in the context of certain mental model construction principles.