This paper presents a new way of formalizing the coalition structure generation problem (CSG) so that we can apply constraint optimization techniques to it. Forming effective coalitions is a major research challenge in AI and multi-agent systems. CSG involves partitioning a set of agents into coalitions to maximize social surplus. Traditionally, the input of the CSG problem is a black-box function called a characteristic function, which takes a coalition as input and returns the value of the coalition. As a result, applying constraint optimization techniques to this problem has been infeasible. However, characteristic functions that appear in practice often can be represented concisely by a set of rules, rather than treating the function as a black box. Then we can solve the CSG problem more efficiently by directly applying constraint optimization techniques to this compact representation. We present new formalizations of the CSG problem by utilizing recently developed compact representation schemes for characteristic functions. We first characterize the complexity of CSG under these representation schemes. In this context, the complexity is driven more by the number of rules than by the number of agents. As an initial step toward developing efficient constraint optimization algorithms for solving the CSG problem, we also develop mixed integer programming formulations and show that an off-the-shelf optimization package can perform reasonably well.

The training program of high school teachers in Japan lacks the chance to practice teaching skills and the admission of classes. The result is, many young teachers left their jobs in the first year due to frustration and other mental issues. In order to relieve this issue, we are running a project to develop a platform of virtual school environment and virtual students which allows the teacher trainees to practice with. As part of this platform, in order to send feedbacks to the trainee to stimulate them to improve their performance, we propose the use of the whole group of virtual students to generate per- ceivable atmospheres. Three research issues then emerge:(1) Whether the atmospheres in a classroom can be expressed in a computational (parameterized) model? (2) If the answer to (1) is 'yes', what are the elements to construct atmospheres? (3) The perception of atmospheres can be considered as very subjective, how to make the model objective becomes the last research issue. This paper presents our investigation on these issues and propose a parameterized atmosphere generation model based on empirical results.

In recent years, companies are seeking for communication skill from their employers. More and more companies adopt group discussions in employer recruitment to evaluate the ap- plicants' communication skill. However, the opportunity to improve communication skill in group discussion is limited due to the lack of partners. In order to solve this issue, our ongoing project is aiming to build a virtual agent or a robot that can participate group discussion, so that its users can re- peatedly practice group discussion with it. In this paper, we propose the models in directing the agent's attention toward the other participants in three situations:when the agent is speaking, when the agent is listening, and when no partic- ipant is speaking. First, we gathered a data corpus of the discussion of 10 four-people groups. We then use low-level non-verbal features including attention of other participant, voice prosody, head movements, and speech turn extracted in the 10-hour corpus to train support vector machine models to determine the agent's attention on the other participants, or the material. The performance of the detection models in F-measure range between 0.4 and 0.6.

While the environment of schools become more and more complicated, the improvement of teachers' skills in teaching and management is required. In this study, we focus on the development of a Wizard-of-Oz (WOZ) platform of simulated school environment, which can be utilized for teacher training or the examination of teacher recruitment from remote. This system is comprised of two front ends, one is a simulated classroom for the trainee, the other one is the interface for the system operator / investigator. The virtual classroom contains a number of virtual students who are controlled by the operator from the remote. The operator can observe the trainee from a dedicated interface and control the behaviors of any individual student as well as the atmosphere of the whole class. The whole-class atmosphere created by relatively large number of students is modeled as a concentration-arousal two dimensional space. The prototype system is evaluated with subject experiment and the results are reported.