We develop a real-time algorithm based on a Monte-Carlo game tree search for solving a quantified constraint satisfaction problem (QCSP), which is a CSP where some variables are universally quantified. A universally quantified variable represents a choice of nature or an adversary. The goal of a QCSP is to make a robust plan against an adversary. However, obtaining a complete plan off-line is intractable when the size of the problem becomes large. Thus, we need to develop a real-time algorithm that sequentially selects a promising value at each deadline. Such a problem has been considered in the field of game tree search. In a standard game tree search algorithm, developing a good static evaluation function is crucial. However, developing a good static evaluation function for a QCSP is very difficult since it must estimate the possibility that a partially assigned QCSP is solvable. Thus, we apply a Monte-Carlo game tree search technique called UCT. However, the simple application of the UCT algorithm does not work since the player and the adversary are asymmetric, i.e., finding a game sequence where the player wins is very rare. We overcome this difficulty by introducing constraint propagation techniques. We experimentally compare the winning probability of our UCT-based algorithm and the state-of-the-art alpha-beta search algorithm. Our results show that our algorithm outperforms the state-of-the-art algorithm in large-scale problems.

The Borda voting rule is a positional scoring rule where, for m candidates, for every vote the first candidate receives m-1 points, the second m-2 points and so on. A Borda winner is a candidate with highest total score. It has been a prominent open problem to determine the computational complexity of Unweighted Coalitional Manipulation under Borda: Can one add a certain number of additional votes (called manipulators) to an election such that a distinguished candidate becomes a winner? We settle this open problem by showing NP-hardness even for two manipulators and three input votes. Moreover, we discuss extensions and limitations of this hardness result.

The growing number of statistical topic models led to the need to better evaluate their output. Traditional evaluation means estimate the model’s fitness to unseen data. It has recently been proven than the output of human judgment can greatly differ from these measures. Thus the need for methods that better emulate human judgment is stringent. In this paper we present a system that computes the usefulness of individual topics from a given model on the basis of information drawn from a given ontology, in this case WordNet. The notion of utility is regarded as the ability to attribute a concept to each topic and separate words related to the topic from the unrelated ones based on that concept. In multiple experiments we prove the correlation between the automatic evaluation method and the answers received from human evaluators, for various corpora and difficulty levels. By changing the evaluation focus from a statistical one to a conceptual one we were able to detect which topics are conceptually meaningful and rank them accordingly.

The robot programming and plan language IndiGolog allows for on-line execution of actions and offline projections of programs in dynamic and partly unknown environments. Basic assumptions are that the outcomes of primitive and sensing actions are correctly modeled, and that the agent is informed about all exogenous events beyond its control. In real-world applications, however, such assumptions do not hold. In fact, an action’s outcome is error-prone and sensing results are noisy. In this paper, we present a belief management system in IndiGolog that is able to detect inconsistencies between a robot’s modeled belief and what happened in reality. The system furthermore derives explanations and maintains a consistent belief. Our main contributions are (1) a belief management system following a history-based diagnosis approach that allows an agent to actively cope with faulty actions and the occurrence of exogenous events; and (2) an implementation in IndiGolog and experimental results from a delivery domain.

We develop a novel circuit-level stochastic local search (SLS) method D-CRSat for Boolean satisfiability by integrating a structure-based heuristic into the recent CRSat algorithm. D-CRSat significantly improves on CRSat on real-world application benchmarks on which other current CNF and circuit-level SLS methods tend to perform weakly. We also give an intricate proof of probabilistically approximate completeness for D-CRSat, highlighting key features of the method.

Monitoring team activity is beneficial when human teams cooperate in the enactment of a joint plan. Monitoring allows teams to maintain awareness of each other's progress within the plan and it enables anticipation of information needs. Humans find this difficult, particularly in time-stressed and uncertain environments. In this paper we introduce a probabilistic model, based on Conditional Random Fields, to automatically recognise the composition of teams and the team activities in relation to a plan. The team composition and activities are recognised incrementally by interpreting a stream of spatio-temporal observations.

In this paper, we propose the new Ball Ranking Machines (BRMs) to address the supervised ranking problems. In previous work, supervised ranking methods have been successfully applied in various information retrieval tasks. Among these methodologies, the Ranking Support Vector Machines (Rank SVMs) are well investigated. However, one major fact limiting their applications is that Ranking SVMs need optimize a margin-based objective function over all possible document pairs within all queries on the training set. In consequence, Ranking SVMs need select a large number of support vectors among a huge number of support vector candidates. This paper introduces a new model of of Ranking SVMs and develops an efficient approximation algorithm, which decreases the training time and generates much fewer support vectors. Empirical studies on synthetic data and content-based image/video retrieval data show that our method is comparable to Ranking SVMs in accuracy, but use much fewer ranking support vectors and significantly less training time.

We consider a framework for preference aggregation on multiple binary issues, where agents' preferences are represented by (possibly cyclic) CP-nets. We focus on the majority aggregation of the individual CP-nets, which is the CP-net where the direction of each edge of the hypercube is decided according to the majority rule. First we focus on hypercube Condorcet winners (HCWs); in particular, we show that, assuming a uniform distribution for the CP-nets, the probability that there exists at least one HCW is at least 1-1/e, and the expected number of HCWs is 1. Our experimental results confirm these results. We also show experimental results under the Impartial Culture assumption. We then generalize a few tournament solutions to select winners from (weighted) majoritarian CP-nets, namely Copeland, maximin, and Kemeny. For each of these, we address some social choice theoretic and computational issues.

Multi-label learning deals with the problem where each training example is represented by a single instance while associated with a set of class labels. For an unseen example, existing approaches choose to determine the membership of each possible class label to it based on identical feature set, i.e. the very instance representation of the unseen example is employed in the discrimination processes of all labels. However, this commonly-used strategy might be suboptimal as different class labels usually carry specific characteristics of their own, and it could be beneficial to exploit different feature sets for the discrimination of different labels. Based on the above reflection, we propose a new strategy to multi-label learning by leveraging label-specific features, where a simple yet effective algorithm named LIFT is presented. Briefly, LIFT constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Extensive experiments across sixteen diversified data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms.

This paper shows how to learn general, Finite State Machine representations of activities that function as recognizers of previously unseen instances of activities. The central problem is to tell which differences between instances of activities are unimportant and may be safely ignored for the purpose of learning generalized representations of activities. We develop a novel way to find the "essential parts" of activities by a greedy kind of multiple sequence alignment, and a method to transform the resulting alignments into Finite State Machine that will accept novel instances of activities with high accuracy.