In the B-League, Japan professional basketball league, foreign players account for a large percentage of the scoring and are the core. Therefore, the strategy of scouting talented foreign players is essential for the teams. However, team managers can not survey all players because there are many foreign leagues and a huge number of players. Therefore, most of the scouting is done through agents, so intermediary costs are significant. We propose an efficient system to find players from the NCAA (National Collegiate Athletic Association) where many of the foreign players in the B-League came from. We focus on the following topics: (1) Classify the playing styles of B-League players using k-means. (2) Estimate NCAA players’ playing styles using the B-League players’ playing style classification model. (3) Estimate efficient lineups using regression. (4) Estimate players available for scouting on a B-League budget based on scoring and age distribution. (5) Simulation for Chiba Jets Funabashi, which is a B-League team. We verified the accuracy of those using play-by-play and boxscore data of B-League from 2015-16 to 2021-22 and NCAA from 2005-06 to 2021-22 seasons. As a result, although some issues have to be resolved, we demonstrated its potential to contribute to the efficiency of player scouting.

Developing a communication agent that can mutually interact with a human has been expected. To realize the agent, real-time situation recognition and motion generation are necessary. The human-human interaction data is utilized to develop the recognition and the generation model. However, a cost of giving a certain label to the data is expensive, i.e., the number of labeled data becomes small. To cope with the small dataset problem, one of the approaches is to obtain the pre-trained weight by self-supervised learning. In this research, we propose estimating the amount of time-shift by “lag operation” as a task for self-supervised learning. The observed data is not isolated during the interaction between two people, and using both observed information from two people makes an estimation model reduce the uncertainty of situation detection. By exploiting these properties of interaction data, the time index of data of one person is shifted, i.e., the entrainment of two data is broken. This operation is called a “lag operation”, and estimating the amount of time-shift is defined as the pre-training task. We apply this pre-training to the prediction experiment that estimates near-future laughing during a conversation. The result shows the accuracy of the laughing prediction is improved by 1.3 points, and the lag operation is an effect for predicting the change of interaction situation.

This paper presents Non-Axiomatic Term Logic (NATL) as a theoretical computational framework of humanlike symbolic reasoning in artificial intelligence. NATL unites a discrete syntactic system inspired from Aristotle’s term logic and a continuous semantic system based on the modern idea of distributed representations, or embeddings. This paper positions the proposed approach in the phylogeny and the literature of logic, and explains the framework. As it is yet no more than a theory and it requires much further elaboration to implement it, no quantitative evaluation is presented. Instead, qualitative analyses of arguments using NATL, some applications to possible cognitive science/robotics-related research, and remaining issues towards a machinery implementation are discussed.

Outcome estimation of treatments for individual targets is a crucial foundation for decision making based on causal relations. Most of the existing outcome estimation methods deal with binary or multiple-choice treatments; however, in some applications, the number of interventions can be very large, while the treatments themselves have rich information. In this study, we consider one important instance of such cases, that is, the outcome estimation problem of graph-structured treatments such as drugs. Due to the large number of possible interventions, the counterfactual nature of observational data, which appears in conventional treatment effect estimation, becomes a more serious issue in this problem. Our proposed method GraphITE (pronounced ‘graphite’) obtains the representations of the graph-structured treatments using graph neural networks, and also mitigates the observation biases by using the HSIC regularization that increases the independence of the representations of the targets and the treatments. In contrast with the existing methods, which cannot deal with “zero-shot” treatments that are not included in observational data, GraphITE can efficiently handle them thanks to its capability of incorporating graph-structured treatments. The experiments using the two real-world datasets show GraphITE outperforms baselines especially in cases with a large number of treatments.