Werewolves, cheats, and cultural sensitivity

This paper discusses the design and evaluation of the system MIXER (Moderating Interactions for Cross-Cultural Empathic Relationships), which applies a novel approach to the education of children in cultural sensitivity. MIXER incorporates intelligent affective and interactive characters, including a model of a Theory of Mind mechanism, in a simulated virtual world. We discuss the relevant pedagogical approaches, related work, the underlying mind model used for MIXER agents as well as its innovative interaction interface utilising a tablet computer and a pictorial interaction language. We then consider the evaluation of the system, whether this shows it met its pedagogical objectives, and what can be learned from our results.</p

Huggable Communication Medium Maintains Level of Trust during Conversation Game

There have been several attempts in recent years to develop a remote communication device using sensory modalities other than speech that would induce a user’s positive experience with his/her conversation partner. Specifically, Hugvie is a human-shaped pillow as well as a remote communication device enabling users to combine a hugging experience with telecommunication to improve the quality of remote communication.The present research is based on the hypothesis that using Hugvie maintains users’level of trust toward their conversation partners in situations prone to suspicion. Thelevel of trust felt toward other remote game players was compared between participants using Hugvie and those using a basic communication device while playing a modified version of Werewolf, a conversation-based game, designed to evaluate trust. Although there are always winners and losers in the regular version of Werewolf, the rules were modified to generate a possible scenario in which no enemy was present among the players and all players would win if they trusted each other. We examined the effect of using Hugvie while playing Werewolf on players’ level of trust toward each other and our results demonstrated that in those using Hugvie, the level of trust toward other players was maintained

Towards general cooperative game playing

Attempts to develop generic approaches to game playing have been around for several years in the field of Artificial Intelligence. However, games that involve explicit cooperation among otherwise competitive players cooperative negotiation games have not been addressed by current approaches. Yet, such games provide a much richer set of features, related with social aspects of interactions, which make them appealing for envisioning real-world applications. This work proposes a generic agent architecture Alpha to tackle cooperative negotiation games, combining elements such as search strategies, negotiation, opponent modeling and trust management. The architecture is then validated in the context of two different games that fall in this category Diplomacy and Werewolves. Alpha agents are tested in several scenarios, against other state-of-the-art agents. Besides highlighting the promising performance of the agents, the role of each architectural component in each game is assessed. (c) Springer International Publishing AG, part of Springer Nature 2018

Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models

Deception and persuasion play a critical role in long-horizon dialogues between multiple parties, especially when the interests, goals, and motivations of the participants are not aligned. Such complex tasks pose challenges for current Large Language Models (LLM) as deception and persuasion can easily mislead them, especially in long-horizon multi-party dialogues. To this end, we explore the game of Avalon: The Resistance, a social deduction game in which players must determine each other's hidden identities to complete their team's objective. We introduce an online testbed and a dataset containing 20 carefully collected and labeled games among human players that exhibit long-horizon deception in a cooperative-competitive setting. We discuss the capabilities of LLMs to utilize deceptive long-horizon conversations between six human players to determine each player's goal and motivation. Particularly, we discuss the multimodal integration of the chat between the players and the game's state that grounds the conversation, providing further insights into the true player identities. We find that even current state-of-the-art LLMs do not reach human performance, making our dataset a compelling benchmark to investigate the decision-making and language-processing capabilities of LLMs. Our dataset and online testbed can be found at our project website: https://sstepput.github.io/Avalon-NLU/Comment: Accepted to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP, Findings of the Association for Computational Linguistics

Avatar And Self: A Rhetoric Of Identity Mediated Through Collaborative Role-play

This project responds to a problem in scholarship describing the relationship between virtual avatars and their physical users. In Life on the Screen, Sherry Turkle identifies points of slippage wherein the persona of the avatar becomes conflated with the user‘s sense of self to create an authentic self predicated on both real and virtual experiences (Turkle 184-5). Although the conflation of the authentic self with the virtual has provided various affordances for serious games or other pedagogical projects such as classrooms hosted through the game Second Life, the processes enabling identification with an avatar have been largely overlooked. This project examines several layers of influence that affect how users play with identity to create successful social performances within an online community connected to a work of fiction. In doing so, the user must consider his or her own motivations for creating a persona, how these motivations will allow the avatar to achieve social acceptance, and how these social performances connect to the scene created by the work of fiction. Using an online role-playing forum based on a work of fiction as a site of analysis, this project will borrow from game studies, dramatism, and identity theory to create a framework for discussing processes through which users identify with their virtual avatars

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

This paper aims to investigate the open research problem of uncovering the social behaviors of LLM-based agents. To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game. While previous studies have conducted preliminary investigations into gameplay with LLM agents, there lacks research on their social behaviors. In this paper, we present a novel framework designed to seamlessly adapt to Avalon gameplay. The core of our proposed framework is a multi-agent system that enables efficient communication and interaction among agents. We evaluate the performance of our framework based on metrics from two perspectives: winning the game and analyzing the social behaviors of LLM agents. Our results demonstrate the effectiveness of our framework in generating adaptive and intelligent agents and highlight the potential of LLM-based agents in addressing the challenges associated with dynamic social environment interaction. By analyzing the social behaviors of LLM agents from the aspects of both collaboration and confrontation, we provide insights into the research and applications of this domain

An exploratory study of the problem solving strategies used by selected young adolescents while playing a microcomputer text adventure

The purpose of this study was to determine if any problem solving strategies are used by young adolescents as they play a microcomputer text adventure. Thirteen participants took part in the study. Each participant was video taped playing a selected adventure game, specially modified to produce a file containing all interaction between participant and computer. The investigator analyzed both the files and video tapes to determine problem solving strategies used by each participant. Ten specific problem solving strategies were identified as used by the participants. The use of specific problem solving strategies took place 58% of the game playing time with the specific strategy of Guess and Check occupying 77.7% of that time. Participants took inconsequential moves 30.4% of the game playing time, and made moves using previous game experiences 11.6% of the time. Based on the recorded data, determination was made that young adolescents use a variety of problem solving strategies as they play a microcomputer text adventure

TAG: A Tabletop Games Framework

Esta ponencia forma parte de : 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020)Tabletop games come in a variety of forms, including board games, card games, and dice games. In recent years, their complexity has considerably increased, with many components, rules that change dynamically through the game, diverse player roles, and a series of control parameters that influence a game’s balance. As such, they also encompass novel and intricate challenges for Artificial Intelligence methods, yet research largely focuses on classical board games such as chess and Go. We introduce in this work the Tabletop Games (TAG) framework, which promotes research into general AI in modern tabletop games, facilitating the implementation of new games and AI players, while providing analytics to capture the complexities of the challenges proposed. We include preliminary results with sample AI players, showing some moderate success, with plenty of room for improvement, and discuss further developments and new research direction

Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league