Publisher：Cold Spring Harbor Laboratory  

ABSTRACT

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed as an advanced hunting strategy requiring large brains that involve high level cognition. However, recent findings that collaborative hunting have also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes using computational multi-agent simulation based on deep reinforcement learning. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between observations and actions via distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our results of computational ecology emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

DOI： 10.1101/2022.10.10.511517

Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>Cooperative hunting is a widespread form of cooperation in nature, and it is known that the level of organization of this predation varies among species. However, how cooperative forms of predation have evolved and been maintained is not well understood. In this study, we addressed this issue using a multi-agent simulation based on deep reinforcement learning. We examined changes in behavioral strategies when changing factors that have been suggested to be associated with predation forms by previous observations in nature, and found that the highest level of organization with role division among individuals was emerged under the combined conditions of two factors: difficulty of prey capture, and food (reward) sharing. These results suggest that sophisticated predation forms, which have been thought to require high cognition, can evolve from relatively simple cognitive and learning mechanisms, and emphasize the close link between the predation form and the environment where the organism lives.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b03

CiNii Research

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>Soccer is a game in which many players and the ball interact in complex ways. Regarding the quantitative evaluation of soccer attackers, there have been many and few studies on the player with and without the ball, respectively. However, it is still difficult to evaluate an attacking player without the ball and intention to receive it, and to reveal how movement contributes to the creation of scoring opportunities compared to typical (or predicted) movements. In this paper, we evaluate players who create off-ball scoring opportunities by comparing the reference movements generated by trajectory prediction with actual movements. In the proposed method, first, the trajectory is predicted using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the existing off-ball evaluation index between the actual data and the predicted trajectory, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement as a reference. In the verification, we show that the evaluation of the proposed method is intuitive, using the relationship with the scores with all 18 teams in the Japanese professional soccer league and the example of one game.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b05

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)  

Other Link： https://dblp.uni-trier.de/rec/conf/nips/2021

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<title>Abstract</title>Skilled interception behavior often relies on accurate predictions of external objects because of a large delay in our sensorimotor systems. To deal with the sensorimotor delay, the brain predicts future states of the target based on the current state available, but it is still debated whether internal representations acquired from prior experience are used as well. Here we estimated the predictive manner by analyzing the response behavior of a pursuer to a sudden directional change of the evasive target, providing strong evidence that prediction of target motion by the pursuer was incompatible with a linear extrapolation based solely on the current state of the target. Moreover, using neural network models, we validated that nonlinear extrapolation as estimated was computationally feasible and useful even against unknown opponents. These results support the use of internal representations in predicting target motion, suggesting the usefulness and versatility of predicting external object motion through internal representations.

DOI： 10.1007/s00422-021-00891-9

Web of Science

Scopus

PubMed

Other Link： https://link.springer.com/article/10.1007/s00422-021-00891-9/fulltext.html

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>Skilled interception behavior often relies on accurate predictions of external objects because there is a large delay in our sensorimotor systems. To deal with the sensorimotor delay, the brain needs to predicts future states of the target based on the current state available, but it is not well understood how we predict the motion of interactive targets such as evasive opponents. Here we conduct one-on-one chase and escape task and estimated the predictive manner of pursuer by analyzing the response behavior to a sudden directional change of evader. In addition, we verified that a prediction as estimated is computationally feasible using neural network models. These suggest that the predictive mechanisms that humans use to compensate for sensorimotor delays during pursuit are more sophisticated than previously thought and that the internal representation acquired through prior experience in similar situations may be useful even in predicting the motion of unknown opponent.</p>

DOI： 10.11517/pjsai.jsai2021.0_2d1os604

CiNii Research

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Language：Japanese  

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)