We consider the laboratory assignment problem in which laboratories have minimum and maximum quotas. MSDA proposed by Fragiadakis, et al., is an efficient algorithm to solve the laboratory assignment problem, but it is incomplete to find a fair assignment. In this paper, we show three extensions of the laboratory assignment problem and translations from the extensions to constraint optimization problems. The first extension enables a completeness to find a fair assignment if it exists, but loses strategy-proofness. The original and first extended laboratory assignment problem may have no fair assignment. This is caused by redundant claims of an empty seat. The second extension based on the first one ensures that the laboratory assignment problem has at least one fair assignment by making a claim of an empty seat stricter. The third extension introduces tied ranks to students' preferences over laboratories, for example, students can specify multiple laboratories as their first choice. This extension gives the Žexibility to specify students' preferences and makes it possible to find more desirable assignments. The experimental results show that our approaches requires more computational time compared with MSDA, but can always find a fair assignment and a more desirable one.
Dialogue breakdown detection is a technique used for identifying inappropriate utterances in dialogue systems. Although it is generally assumed that dialogue breakdown detection avoids generating system responses that then cause difficulties in continuing the given dialogue, this has yet to be verified. In this paper, we apply the dialogue breakdown detection technique to generate responses for a chat-oriented dialogue system and experimentally verify that performance is improved by measuring the extent to which dialogue breakdown is avoided. Our experimental results show that naturalness of dialogue and user satisfaction level are improved but enjoyment of dialogue is deteriorated when using this technique.
In this paper, we describe a method to compute multiple improvement scenarios for Multi-Agent Simulation using parameter search by metaheuristic. Specifically, we try to construct multiple scenarios to acquire more customers by using modeling of taxi driver’s behavior from probe car data. The main proposal of this paper is not to find an optimal solution but an approach to lead multiple suboptimal solutions. We construct a taxi agent model that can represent individual traffic behaviors and apply the model to urban traffic multi-agent simulation where general vehicles, bus, and taxis co-exist in the realistic road network of the city of Kyoto, Japan. Then, we try to construct multiple improvement scenarios concerning the sales strategy by multipoint local search method and clustering of parameters. The proposed method works usefully in the problem that the solution property with wide solution space is unknown. In addition, this method has scalability, and it is possible to obtain a better optimum solution by increasing the number of cluster calculators and increasing the number of searching points.
We propose a new approach to discovering regions optimizing the expected responses from data with a strong spatial bias. The methods available thus far do not work well on data of that nature because they assume that coordinates and responses are uniform and isotropic. To relax this assumption, we employ a hypothesis that cells in an irregularly sized mesh are connected transitively. However, it requires considerable computation and possibly overfits data because there are exponentially many transitive closures. Our contributions to overcome these problems are twofold: we prove the maximal property that shows how irrelevant cells are removed without enumerating candidates in the hypothesis space, and we propose a description length of transitive closure based on which the remaining regions are regularized. We show via experiments that our algorithms do not decrease the precision with unknown data, even when such data are neither uniform nor isotropic. In addition, we show that the regularized region improves the precision by more than 20% compared to the unregularized one.