This paper proposes an effective approach to model the emotional space of words to infer their Sense Sentiment Similarity (SSS). SSS reflects the distance between the words regarding their senses and underlying sentiments. We propose a probabilistic approach that is built on a hidden emotional model in which the basic human emotions are considered as hidden. This leads to predict a vector of emotions for each sense of the words, and then to infer the sense sentiment similarity. The effectiveness of the proposed approach is investigated in two Natural Language Processing tasks: Indirect yes/no Question Answer Pairs Inference and Sentiment Orientation Prediction.

Security games involving the allocation of multiple security resources to defend multiple targets generally have an exponential number of pure strategies for the defender. One method that has been successful in addressing this computational issue is to instead directly compute the marginal probabilities with which the individual resources are assigned (first pursued by Kiekintveld et al. (2009)). However, in sufficiently general settings, there exist games where these marginal solutions are not implementable, that is, they do not correspond to any mixed strategy of the defender. In this paper, we examine security games where the defender tries to monitor the vertices of a graph, and we show how the type of graph, the type of schedules, and the type of defender resources affect the applicability of this approach. In some settings, we show the approach is applicable and give a polynomial-time algorithm for computing an optimal defender strategy; in other settings, we give counterexample games that demonstrate that the approach does not work, and prove NP-hardness results for computing an optimal defender strategy.

This paper presents a novel framework for online incremental place recognition in an indoor environment. The framework addresses the scenario in which scene images are gradually obtained during long-term operation in the real-world indoor environment. Multiple users may interact with the classification system and confirm either current or past prediction results; the system then immediately updates itself to improve the classification system. This framework is based on the proposed \emph{n}-value self-organizing and incremental neural network (\emph{n}-SOINN), which has been derived by modifying the original SOINN to be appropriate for use in scene recognition. The evaluation was performed on the standard MIT 67-category indoor scene dataset and shows that the proposed framework achieves the same accuracy as that of the state-of-the-art offline method, while the computation time of the proposed framework is significantly faster and fully incremental update is allowed. Additionally, a small extra set of training samples is incrementally given to the system to simulate the incremental learning situation. The result shows that the proposed framework can leverage such additional samples and achieve the state-of-the-art result.

The tractability of a Constraint Satisfaction Problem (CSP)is guaranteed by a direct relationship between its consistencylevel and a structural parameter of its constraint network suchas the treewidth. This result is not widely exploited in practicebecause enforcing higher-level consistencies can be costlyand can change the structure of the constraint network andincrease its width. Recently, R(*,m)C was proposed as a relational consistency property that does not modify the structureof the graph and, thus, does not affect its width. In this paper,we explore two main strategies, based on a tree decomposition of the CSP, for improving the performance of enforcingR(*,m)C and getting closer to the above tractability condition. Those strategies are: a) localizing the application ofthe consistency algorithm to the clusters of the tree decomposition, and b) bolstering constraint propagation betweenclusters by adding redundant constraints at their separators,for which we propose three new schemes. We characterizethe resulting consistency properties by comparing them, theoretically and empirically, to the original R(*,m)C and thepopular GAC and maxRPWC, and establish the benefits ofour approach for solving difficult problems.

We study hybrid online-batch matching problems, where agents arrive continuously, but are only matched in periodic rounds, when many of them can be considered simultaneously. Agents not getting matched in a given round remain in the market for the next round. This setting models several scenarios of interest, including many job markets as well as kidney exchange mechanisms. We consider the social utility of two commonly used mechanisms for such markets: one that aims for stability in each round (greedy), and one that attempts to maximize social utility in each round (max-weight). Surprisingly, we find that in the long term, the social utility of the greedy mechanism can be higher than that of the max-weight mechanism. We hypothesize that this is because the greedy mechanism behaves similarly to a soft threshold mechanism, where all connections below a certain threshold are rejected by the participants in favor of waiting until the next round. Motivated by this observation, we propose a method to approximately calculate the optimal threshold for an individual agent to use based on characteristics of the other agents participating, and demonstrate experimentally that social utility is high when all agents use this strategy. Thresholding can also be applied by the mechanism itself to improve social welfare; we demonstrate this with an example on graphs that model pairwise kidney exchange.

The increased demand for distributed computations on “big data” has led to solutions such as SCOPE, DryadLINQ, Pig, and Hive, which allow the user to specify queries in an SQL-like language, enriched with sets of user-defined operators. The lack of exact semantics for user-defined operators interferes with the query optimization process, thus putting the burden of suggesting, at least partial, query plans on the user. In an attempt to ease this burden, we propose a formal model that allows for data-parallel program synthesis (DPPS) in a semantically well-defined manner. We show that this model generalizes existing frameworks for data-parallel computation, while providing the flexibility of query plan generation that is currently absent from these frameworks. In particular, we show how existing, off-the-shelf, AI planning tools can be used for solving DPPS tasks.

Despite committees and elections being widespread in thereal-world, the design of agents for operating in humancomputer committees has received far less attention than thetheoretical analysis of voting strategies. We address this gapby providing an agent design that outperforms other voters ingroups comprising both people and computer agents. In oursetting participants vote by simultaneously submitting a ranking over a set of candidates and the election system uses a social welfare rule to select a ranking that minimizes disagreements with participants’ votes. We ran an extensive studyin which hundreds of people participated in repeated votingrounds with other people as well as computer agents that differed in how they employ strategic reasoning in their votingbehavior. Our results show that over time, people learn todeviate from truthful voting strategies, and use heuristics toguide their play, such as repeating their vote from the previous round. We show that a computer agent using a bestresponse voting strategy was able to outperform people in thegame. Our study has implication for agent designers, highlighting the types of strategies that enable agents to succeedin committees comprising both human and computer participants. This is the first work to study the role of computeragents in voting settings involving both human and agent participants.

The Traveling Tournament Problem (TTP) is a well-known NP-complete problem in sports scheduling that was inspired by the application of optimizing schedules for Major League Baseball to reduce total team travel. The techniques and heuristics from the n-team TTP can be extended to optimize the scheduling of other sports leagues, such as the Nippon Professional Baseball (NPB) league in Japan. In this paper, we describe the additional scheduling constraints required by the NPB league, such as the requirement that each team play the same number of weekend home games, weekday home games, weekend road games, and weekday road games. We fully solve this TTP-variant for the case n=6, and conclude the paper by presenting the official 2013 NPB Central League Schedule, where we helped this Japanese baseball league reduce total team travel by over six thousand kilometres.

This paper outlines the design of a scheduling algorithm that allocates outstabling locations to railway trains. From time to time railway trains may need to be outstabled to temporary locations, such as stations, sidings, depots, etc., until they are needed for regular operations. This is common for urban rail transit, and especially so for those that do not operate 24 hours. During non-traffic hours (NTH), trains are outstabled to various locations along the rail network so that when operations start again next day, the trains will be nearby their originating station or conveniently located so that they can be put into service whenever needed. However, this is complicated by the fact that engineering works, such as rail testing, installation, regular maintenance, etc. are done during the NTH. Therefore, passenger trains must be outstabled in such a way that they do not interfere with night-time engineering works or the movements of associated engineering trains. Since the engineering works scheduling is done separate to outstabling, this is a mixed-system problem. This paper shows how we modeled this as a constraint-satisfaction problem (CSP) and implemented into an “Outstabling System” (OSS) for the Hong Kong Mass Transit Railway (MTR) using a two-stage search algorithm.

The learning of evaluation functions from game records has been widely studied in the field of computer game-playing. Conventional learning methods optimize the evaluation function parameters by using the game records of expert players in order to imitate their plays. Such conventional methods utilize objective functions to increase the agreement between the moves selected by game-playing programs and the moves in the records of actual games. The methods, however, have a problem in that increasing the agreement does not always improve the strength of a program. Indeed, it is not clear how this agreement relates to the strength of a trained program. To address this problem, this paper presents a learning method to optimize objective function parameters for strength in game-playing. The proposed method employs an evolutionary learning algorithm with the strengths (Elo ratings) of programs as their fitness scores. Experimental results show that the proposed method is effective since programs using the objective function produced by the proposed method are superior to those using conventional objective functions.