The demand for computer-assisted language learning systems that can provide corrective feedback on language learners’ speaking has increased. However, it is not a trivial task to detect grammatical errors in oral conversations because of the unavoidable errors of automatic speech recognition systems. To provide corrective feedback, a novel method to detect grammatical errors in speaking performance is proposed. The proposed method consists of two sub-models: the grammaticality-checking model and the error-type classification model. We automatically generate grammatical errors that learners are likely to commit and construct error patterns based on the articulated errors. When a particular speech pattern is recognized, the grammaticality-checking model performs a binary classification based on the similarity between the error patterns and the recognition result using the confidence score. The error-type classification model chooses the error type based on the most similar error pattern and the error frequency extracted from a learner corpus. The grammaticality checking method largely outperformed the two comparative models by 56.36% and 42.61% in F-score while keeping the false positive rate very low. The error-type classification model exhibited very high performance with a 99.6% accuracy rate. Because high precision and a low false positive rate are important criteria for the language-tutoring setting, the proposed method will be helpful for intelligent computer-assisted language learning systems.

General Game Players (GGPs) are programs which can play an arbitrary game given only its rules and the Game Description Language (GDL) is a variant of Datalog used in GGP competitions to specify the rules. GDL inherits from Datalog the use of Horn clauses as rules and recursion, but it too requires stratification and does not allow to use quantifiers. We present an alternative formalism for game description which is based on first-order logic (FO). States of the game are represented by relational structures, legal moves by structure rewriting rules guarded by FO formulas, and the goals of the players by formulas which extend FO with counting. The advantage of our formalism comes from more explicit state representationcand from the use of quantifiers in formulas. We show how to exploit existential quantification in players' goals to generate heuristics for evaluating positions in the game. The derived heuristics are good enough for a basic alpha-beta agent to win against state of the art GGP.

Some of these complex queries, such as Q1 or Q2, Recent advances in health and life sciences have led to generation can be represented in a formal query language (e.g., of a large amount of biomedical data. To facilitate access SQL/SPARQL) and then answered using Semantic Web to its desired parts, such a big mass of data has been represented technologies. However, queries, like Q4, that require auxiliary in structured forms, like biomedical ontologies and recursive definitions (such as transitive closure) cannot databases. On the other hand, representing these biomedical be directly represented in these languages; and thus such ontologies and databases in different forms, constructing queries cannot be answered directly using Semantic Web them independently from each other, and storing them at technologies. The experts usually compute auxiliary relations different locations have brought about many challenges for externally, for instance, by enumerating all drug-drug answering queries about the knowledge represented in these interaction chains or gene cliques, and then use these auxiliary ontologies and databases.

When interacting with computer agents, people make inferences about various characteristics of these agents, such as their reliability and trustworthiness. These perceptions are significant, as they influence people's behavior towards the agents, and may foster or inhibit repeated interactions between them. In this paper we investigate whether computer agents can use the expression of emotion to influence human perceptions of trustworthiness. In particular, we study human-computer interactions within the context of a negotiation game, in which players make alternating offers to decide on how to divide a set of resources. A series of negotiation games between a human and several agents is then followed by a "trust game." In this game people have to choose one among several agents to interact with, as well as how much of their resources they will trust to it. Our results indicate that, among those agents that displayed emotion, those whose expression was in accord with their actions (strategy) during the negotiation game were generally preferred as partners in the trust game over those whose emotion expressions and actions did not mesh. Moreover, we observed that when emotion does not carry useful new information, it fails to strongly influence human decision-making behavior in a negotiation setting.

The most prominent way to establish trust between buyers and sellers on online auction sites are reputation mechanisms. Two drawbacks of this approach are the reliance on the seller being long-lived and the susceptibility to whitewashing. In this paper, we introduce so-called escrow mechanisms that avoid these problems by installing a trusted intermediary which forwards the payment to the seller only if the buyer acknowledges that the good arrived in the promised condition. We address the incentive issues that arise and design an escrow mechanism that is incentive-compatible, efficient, interim individually rational and ex ante budget-balanced. In contrast to previous work on trust and reputation, our approach does not rely on knowing the sellers' cost functions or the distribution of buyer valuations.

In this paper, we shed light on how powerful congestion control based on local interactions may be obtained. We show how ants can use repellent pheromones and incorporate the effect of crowding to avoid traffic congestion on the optimal path. Based on these interactions, we propose an ant algorithm, the M-unit EigenAnt algorithm, that leads to the selection of the M shortest paths. The ratio of selection of each of these paths is also optimal and regulated by an optimal amount of pheromone on each of them. To the best of our knowledge, the M -unit EigenAnt algorithm is the first antalgorithm that explicitly ensures the selection of the M shortest paths and regulates the amount of pheromone on them such that it is asymptotically optimal. In fact, it is in contrast with most ant algorithms that aim to discover just a single best path. We provide its convergence analysis and show that the steady state distribution of pheromone aligns with the eigenvectors of the cost matrix, and thus is related to its measure of quality. We also provide analysis to show that this property ensues even when the food is moved or path lengths change during foraging. We show that this behavior is robust in the presence of fluctuations and quickly reflects the change in the M optimal solutions. This makes it suitable for not only distributed applications butalso dynamic ones as well. Finally, we provide simulation results for the convergence to the optimal solution under different initial biases, dynamism in lengths of paths, and discovery of new paths.

Approval-like voting rules, such as Sincere-Strategy Preference-Based Approval voting (SP-AV), the Bucklin rule (an adaptive variant of k-Approval voting), and the Fallback rule (an adaptive variant of SP-AV) have many desirable properties: for example, they are easy to understand and encourage the candidates to choose electoral platforms that have a broad appeal. In this paper, we investigate both classic and parameterized computational complexity of electoral campaign management under such rules. We focus on two methods that can be used to promote a given candidate: asking voters to move this candidate upwards in their preference order or asking them to change the number of candidates they approve of. We show that finding an optimal campaign management strategy of the first type is easy for both Bucklin and Fallback. In contrast, the second method is computationally hard even if the degree to which we need to affect the votes is small. Nevertheless, we identify a large class of scenarios that admit a fixed-parameter tractable algorithm.

The conventional model of coalition formation considers every possible subset of agents as a potential coalition. However, in many real-world applications, there are inherent constraints on feasible coalitions: for instance, certain agents may be prohibited from being in the same coalition, or the coalition structure may be required to consist of coalitions of the same size. In this paper, we present the first systematic study of constrained coalition formation (CCF). We propose a general framework for this problem, and identify an important class of CCF settings, where the constraints specify which groups of agents should/should not work together. We describe a procedure that transforms such constraints into a structured input that allows coalition formation algorithms to identify, without any redundant computations, all the feasible coalitions. We then use this procedure to develop an algorithm for generating an optimal (welfare-maximizing) constrained coalition structure, and show that it outperforms existing state-of-the-art approaches by several orders of magnitude.

Our approach Multi-agent task allocation is an important and challenging yields significant reductions in both run-time and communication, problem, which involves deciding how to assign a set thereby increasing real-world applicability. of agents to a set of tasks, both of which may change over In more detail, in this paper we advance the state-ofthe-art time (i.e., it is a dynamic environment). Moreover, it is often in the following ways: first, we present a novel, necessary for heterogeneous agents to form teams (known as online domain pruning algorithm specifically tailored to coalitions) to complete certain tasks in the environment. In dynamic task allocation environments to reduce the number coalitions, agents can often complete tasks more efficiently of potential solutions that need to be considered.

We propose influence games, a new class of graphical games, as a model of the behavior of large but finite networked populations. Grounded in non-cooperative game theory, we introduce a new approach to the study of influence in networks that captures the strategic aspects of complex interactions in the network. We study computational problems on influence games, including the identification of the most influential nodes. We characterize the computational complexity of various problems in influence games, propose several heuristics for the hard cases, and design approximation algorithms, with provable guarantees, for the most influential nodes problem.