Daisuke Katagami

Investigation of the effects of nonverbal information on werewolf

Werewolf is one of the popular communication games all over the world. It treats ambiguity of human discussion including the utterances, gestures and facial expressions in a broad sense. In this research, we pay attention to this imperfect information game werewolf. The purpose of the research is to develop an intelligent agent “AI werewolf” which is enabled to naturally play werewolf with human. This paper aims to investigate how behavior contribute to victory of own-side players by using machine learning as a first step. As the results of investigation and analysis of the playing movie, we found that nonverbal information in the game of werewolf has importance to winning or losing the game.

Development of werewolf match system for human players mediated with lifelike agents

Are You a Werewolf? is a conversation type game. We construct a Werewolf match system for humans with lifelike agents. We evaluate whether it is possible to realize conversation space of Are You a Werewolf? with the system.

Movement design of a life-like agent for the werewolf game

In this research, we target at the interactive communication game “werewolf” with a subject of research. Werewolf is a popular party game all over the world, and the relevance studies have been advanced in recent years. However, the life-like agent who does werewolf has not been developed. Therefore the purpose of this research is to analyze non-verbal information from movies which play the werewolf with face-to-face communication and to make clear the impression for others by the movements of players in the game. Moreover, we verify whether the life-like agent gives an impression like human in werewolf game by mounting the movements on a life-like agent.

AI Wolf Contest — Development of Game AI Using Collective Intelligence —

In this study, we specify the design of an artificial intelligence (AI) player for a communication game called “Are You a Werewolf?” (AI Wolf). We present the Werewolf game as a standard game problem in the AI field. It is similar to game problems such as Chess, Shogi, Go, and Poker. The Werewolf game is a communication game that requires several AI technologies such as multi-agent coordination, intentional reading, and understanding of the theory of mind. Analyzing and solving the Werewolf game as a standard problem will provide useful results for our research field and its applications. Similar to the RoboCup project, the goal of this project is to determine new themes while creating a communicative AI player that can play the Werewolf game with humans. As an initial step, we designed a platform to develop a game-playing AI for a competition. First, we discuss the essential factors in Werewolf with reference to other studies. We then develop a platform for an AI game competition that uses simplified rules to support the development of AIs that can play Werewolf. The paper reports the process and analysis of the results of the competition.

Constructing a Human-like agent for the Werewolf Game using a psychological model based multiple perspectives

In this paper, we focus on the Werewolf Game. The Werewolf Game is an advanced communication-game in which winning or losing is directly linked to ones success or failure in communication. Therefore, we expect exponential developments in artificial intelligence by studying the Werewolf Game. In this current study, we propose a psychological model that considers multiple perspectives to model the play of a human such as inferring the intention of the other side. As one of the psychological models, we constructed a “ones self model” that models the role of others as viewed from their own viewpoint. In addition, to determine whether ones opinion is reliable after inferring others intentions, we also constructed an “others model” that models the role of others as viewed from their viewpoints. Combining these models, we showed through experimentation that a combined approach achieved better results, i.e., higher win percentages.

Investigation of social adaptive skills by cross-cultural simulation game and KiSS-18

In this paper, we investigated the relationships between the behavior of a player in a cross-cultural simulation game and the social skills of a player by a scale of social skills for the comprehension of social adaptive behavior of human in a social group. The game developed adopted KiSS-18 as a scale of social skills. We showed the relationships between the changes of a players behavior and the measured results by KiSS-18, and discussed the feasibility of the determination of social adaptive skills by interactions with fourteen agents in the game.

Werewolf Game Modeling Using Action Probabilities Based on Play Log Analysis

In this study, we construct a non-human agent that can play the werewolf game (i.e., AI wolf) with aims of creating more advanced intelligence and acquire more advanced communication skills for AI-based systems. We therefore constructed a behavioral model using information regarding human players and the decisions made by such players; all such information was obtained from play logs of the werewolf game. To confirm our model, we conducted simulation experiments of the werewolf game using an agent based on our proposed behavioral model, as well as a random agent for comparison. Consequently, we obtained an 81.55% coincidence ratio of agent behavior versus human behavior.

Generating human-like discussion by paraphrasing a translation by the AIWolf protocol using werewolf BBS logs

“Are you a werewolf?” is one of the most popular communication games and is played globally. The AIWolf Project developed an agent, named “theAIWolf,” that can play “Areyou a werewolf?”. An AIWolf utters its thoughts using an AIWolf Protocol. As it is difficult for humans to understand the AIWolf Protocol, translation into natural language is required when human players are involved. However, the conventional method of translation uses a word-to-word method, creating the impression that the utterances have been generated by a machine. This study aimed make the utterances of AIWolf sound more human. The authors set the target that a human player would be unable to distinguish human speech from that generated by AIWolves (the Turing test). The authors define the situation as the maximum value of humanity. The output of translated AIWolf Protocol was paraphrased using data from Werewolf BBS Logs. This study considers making the utterances of AIWolf sound more human using Werewolf BBS Logs and a possibility assignment equation with fuzzy sets. In this paper, an experiment was conducted to confirm whether paraphrasing the utterances of AIWolf using Werewolf BBS Logs for human-like speech is useful or not. It was shown that the experimental method produced slightly more human-like speech than the conventional method.

Improving Relationships Based on Positive Politeness Between Humans and Life-Like Agents

In interpersonal interactions, humans speak in part by considering their social distance and position with respect to other people, thereby developing relationships. In our research, we focus on positive politeness (PP), a strategy for positively reducing the distance people in human communication using language. In addition, we propose an agent that attempts to actively interact with humans. First, we design a dialog system based on the politeness theory. Next, we examine the effect of our proposed method on interactions. For our experiments, we implemented two agents:the method proposed for performing PP and a conventional method that performs negative politeness based on the unobjectionable behavior. We then compare and analyze impressions of experiment participants in response to the two agents. From our results, male participants accepted PP more frequently than female participants. Further, the proposed method lowered the perceived sense of interacting with a machine for male participants.

IWME 2014: first international workshop on mood engineering

The first international workshop on mood engineering (IWME) is held in Tsukuba, Japan on October 28, 2014 and co-located with the second international conference on human-agent interaction (HAI). The main objective of the workshop is to create artificial moods by robot and agents and to clarify the mechanism of a mood created by a group including humans, robo ts, and agents. We have constructed an exciting program of demonstrations, posters, and video materials as interactive sessions that will provide participants creative a mood for collaborative research.