Computer agents versus avatars: Responses to interactive game characters controlled by a computer or other player

Computer and videogames often require that users interact with other characters on the screen that represent other real people or characters that are controlled by computer code running within the game. The difference between game play with other avatars (player-controlled characters) or agents (characters controlled by the computer) may influence the engagement a game player experiences. This study investigated the effects of agency (avatar versus agent) and the type of gaming activity (competition versus cooperation) on physiological arousal and subjective evaluation of play. A 2 (avatar, agent)×2 (competition, cooperation) within-subject experiment was conducted (N=32). Players exhibited greater physiological arousal to otherwise identical interactions when other characters were introduced as an avatar rather than an agent. Furthermore, the co-player's source of agency interacted with the type of gaming activity. The results have implications for understanding how different forms of representation in virtual worlds and games will affect psychological responses in the contexts of entertainment, learning and the conduct of serious work.     

Extending Answer Sets for Logic Programming Agents

We present systems of logic programming agents (LPAS) to model the interactions between decision-makers while evolving to a conclusion. Such a system consists of a number of agents connected by means of unidirectional communication channels. Agents communicate with each other by passing answer sets obtained by updating the information received from connected agents with their own private information. We introduce a credulous answer set semantics for logic programming agents. As an application, we show how extensive games with perfect information can be conveniently represented as logic programming agent systems, where each agent embodies the reasoning of a game player, such that the equilibria of the game correspond with the semantics agreed upon by the agents in the LPAS.     

Social interaction for efficient agent learning from human reward

Learning from rewards generated by a human trainer observing an agent in action has been proven to be a powerful method for teaching autonomous agents to perform challenging tasks, especially for those non-technical users. Since the efficacy of this approach depends critically on the reward the trainer provides, we consider how the interaction between the trainer and the agent should be designed so as to increase the efficiency of the training process. This article investigates the influence of the agent’s socio-competitive feedback on the human trainer’s training behavior and the agent’s learning. The results of our user study with 85 participants suggest that the agent’s passive socio-competitive feedback—showing performance and score of agents trained by trainers in a leaderboard—substantially increases the engagement of the participants in the game task and improves the agents’ performance, even though the participants do not directly play the game but instead train the agent to do so. Moreover, making this feedback active—sending the trainer her agent’s performance relative to others—further induces more participants to train agents longer and improves the agent’s learning. Our further analysis shows that agents trained by trainers affected by both the passive and active social feedback could obtain a higher performance under a score mechanism that could be optimized from the trainer’s perspective and the agent’s additional active social feedback can keep participants to further train agents to learn policies that can obtain a higher performance under such a score mechanism.     

A Dialogue Game Protocol for Multi-Agent Argument over Proposals for Action

We present the syntax and semantics for a multi-agent dialogue game protocol which permits argument over proposals for action. The protocol, called the Persuasive Argument for Multiple Agents (PARMA) Protocol, embodies an earlier theory by the authors of persuasion over action which enables participants to rationally propose, attack, and defend, an action or course of actions (or inaction). We present an outline of both an axiomatic and a denotational semantics, and discuss implementation of the protocol, in the context of both human and artificial agents.     

基于合作博弈的移动代理网管任务协作模型

为提高网络管理任务性能,需要研究在复杂网管任务下多移动代理协作问题 .由于传统代理协作模型(如合同网协议)并不适合大规模网络中复杂任务的代理协作,不能保证协作模型中个体代理性能的稳定 .为此合作博弈理论成为移动代理的网管任务协作问题的重要途径,在该协作模型中,单个功能代理被视为具有自主意识的主体,它具有自身的效用函数评估个体的性能 .将代理协作问题转化成为凸联盟博弈模型并利用Shapley值作为协作模型中任务分配合理性的评判标准,并基于上述理论模型,提出3阶段的任务协作算法 .     

Expressing and interpreting emotional movements in social games with robots

This paper provides a framework for recording, analyzing and modeling of 3 dimensional emotional movements for embodied game applications. To foster embodied interaction, we need interfaces that can develop a complex, meaningful understanding of intention—both kinesthetic and emotional—as it emerges through natural human movement. The movements are emulated on robots or other devices with sensory-motor features as a part of games that aim improving the social interaction skills of children. The design of an example game platform that is used for training of children with autism is described since the type of the emotional behaviors depends on the embodiment of the robot and the context of the game. The results show that quantitative movement parameters can be matched to emotional state of the embodied agent (human or robot) using the Laban movement analysis. Emotional movements that were emulated on robots using this principle were tested with children in the age group 7–9. The tests show reliable recognition on most of the behaviors.     

NDM-Based Cognitive Agents for Supporting Decision-Making Teams

Naturalistic decision making (NDM) focuses on how people actually make decisions in realistic settings that typically involve ill-structured problems. Taking an experimental approach, we investigate the impacts of using an NDM-based software agent (R-CAST) on the performance of human decision-making teams in a simulated C3I (Communications, Command, Control and Intelligence) environment. We examined four types of decision-making teams with mixed human and agent members playing the roles of intelligence collection and command selection. The experiment also involved two within-group control variables: task complexity and context switching frequency. The result indicates that the use of an R-CAST agent in intelligence collection allows its team member to consider the latest situational information in decision making but might increase the team member's cognitive load. It also indicates that a human member playing the role of command selection should not rely too much on the agent serving as his or her decision aid. Together, it is suggested that the roles of both humans and cognitive agents are critical for achieving the best possible performance of C3I decision-making teams: Whereas agents are superior in computation-intensive activities such as information seeking and filtering, humans are superior in projecting and reasoning about dynamic situations and more adaptable to teammates' cognitive capacities. This study has demonstrated that cognitive agents empowered with NDM models can serve as the teammates and decision aids of human decision makers. Advanced decision support systems built upon such team-aware agents could help achieve reduced cognitive load and effective human-agent collaboration.     

A hybrid fuzzy logic/constraint satisfaction problem approach to automatic decision making in simulation game models

Possible techniques for representing automatic decision-making behavior approximating human experts in complex simulation model experiments are of interest. Here, fuzzy logic (FL) and constraint satisfaction problem (CSP) methods are applied in a hybrid design of automatic decision making in simulation game models. The decision processes of a military headquarters are used as a model for the FL/CSP decision agents choice of variables and rulebases. The hybrid decision agent design is applied in two different types of simulation games to test the general applicability of the design. The first application is a two-sided zero-sum sequential resource allocation game with imperfect information interpreted as an air campaign game. The second example is a network flow stochastic board game designed to capture important aspects of land manoeuvre operations. The proposed design is shown to perform well also in this complex game with a very large (billionsize) action set. Training of the automatic FL/CSP decision agents against selected performance measures is also shown and results are presented together with directions for future research.     

Intelligent control of external software systems

This paper focuses on the relatively unexplored set of issues that arises when an intelligent agent attempts to use external software systems (EESs). The issues are illustrated initially in the context of the complex agent-ESS interactions in an engineering design example. Approaching the area from the perspective of artificial intelligence (AI) research, we find that in general, agent-ESS interactions vary widely. We characterize the possible variations in terms of performance capabilities required, skill levels at which performance is exhibited, and knowledge sources from which capabilities can be acquired. We are exploring these variations using Soar as our candidate AI agent; the document briefly describes seven Soar-based projects in early stages of development, in which agent-ESS issues are addressed. We conclude by placing agent-ESS research in the context of other work on software technology, and discuss the research agenda we have set for ourselves in this area.     

Competing agents in agent-mediated institutions

Social processes and agent interaction always take place in a specific context. A school of thought in social studies analyses them in the framework of institutions. We present in this paper the notion ofagentmediated institutions and show how it is relevant for multi-agent systems (MAS) in general and, more specifically, for MAS that include human agents and software agents involved in socioeconomic interactions. We show how the social interactions of human and software agents taking place in the Cohabited Mixed-Reality Information Spaces (COMRIS) project can be described as such an institution, the Conference Centre institution.     

“She is just stupid”—Analyzing user–agent interactions in emotional game situations

A multiplayer dice game was realized which is played by two users and one embodied conversational agent. During the game, the players have to lie to each other to win the game and the longer the game commences the more probable it is that someone is lying, which creates highly emotional situations. We ran a number of evaluation studies with the system. The specific setting allows us to compare user–user interactions directly with user–agent interactions in the same game. So far, the users’ gaze behavior and the users’ verbal behavior towards one another and towards the agent have been analyzed. Gaze and verbal behavior towards the agent partly resembles patterns found in the literature for human–human interactions, partly the behavior deviates from these observations and could be interpreted as rude or impolite like continuous staring, insulting, or talking about the agent. For most of these seemingly abusive behaviors, a more thorough analysis reveals that they are either acceptable or present some interesting insights for improving the interaction design between users and embodied conversational agents.     

A future framework for interfacing BDI agents in a real-time teaming environment

Research and applications in human–machine teaming continue to evolve the role of the human from immediate (manual) operator into supervisory and televisory controller. In the supervisory control role, the human operator will be functionally removed from the system under control and in the televisory role, the human operator will be physically removed. Although unmanned systems and vehicles have become a technical reality that drives this change, they will not eliminate the importance of the human operator as the commanding and controlling element in-the-loop. This paper will argue that existing automation concepts remain equally valid with an even greater emphasis on the need for a human-centered automation approach. Intelligent agent technology has become mature and attractive enough to implement the automated components of the human–machine team. Agents that implement the Beliefs-Desire-Intention syntax will be discussed as being of particular interest for human–machine teaming applications. This paper proposes a theoretical framework for teaming human and intelligent agents. The teaming framework will be demonstrated in a real-time simulation environment using the commercial game called Unreal Tournament and its existing GameBot extension. The intelligent agents will be implemented based on the Belief-Desire-Intention (BDI) syntax and using JACK, a commercial BDI Agent development language. The requirements for follow-on research, such as human–agent teaming, human–agent coordination and agent learning will be highlighted.     

Video game values: Human–computer interaction and games

Current human–computer interaction (HCI) research into video games rarely considers how they are different from other forms of software. This leads to research that, while useful concerning standard issues of interface design, does not address the nature of video games as games specifically. Unlike most software, video games are not made to support external, user-defined tasks, but instead define their own activities for players to engage in. We argue that video games contain systems of values which players perceive and adopt, and which shape the play of the game. A focus on video game values promotes a holistic view of video games as software, media, and as games specifically, which leads to a genuine video game HCI.     

Forecasting market prices in a supply chain game

Predicting the uncertain and dynamic future of market conditions on the supply chain, as reflected in prices, is an essential component of effective operational decision-making. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the trading agent competition supply chain management game (TAC/SCM). We employ a variety of machine learning and representational techniques to exploit as many types of information as possible, integrating well-known methods in novel ways. We evaluate these techniques through controlled experiments as well as performance in both the main TAC/SCM tournament and supplementary Prediction Challenge. Our prediction methods demonstrate strong performance in controlled experiments and achieved the best overall score in the Prediction Challenge.     

一个基于博弈学习的多主体竞价模型

根据多主体撮合交易模型,把整个撮合交易看成各交易主体的动态交互过程,设计了基于Multi-Agent的电子商务交易市场中交易主体动态竞价策略,提出了博弈学习的概念,并建立了基于博弈学习的动态竞价模型,根据撮合密度的定义,分析了所建立模型的性能和效率,试验表明,基于博弈学习的多主体动态竞价模型使多主体撮合交易系统具有一定的自均衡和自学习能力和良好的交易性能。     

Execution infrastructure for normative virtual environments

Virtual Institutions (VIs) have proven to be adequate to engineer applications where participants can be humans and software agents. VIs combine Electronic Institutions (EIs) and 3D Virtual Worlds (VWs). In this context, Electronic Institutions are used to establish the regulations that structure interactions and support software agent participation while Virtual Worlds facilitate human participation. In this paper we propose Virtual Institution eXEcution Environment (VIXEE) as an innovative communication infrastructure for VIs. Using VIXEE to connect Virtual Worlds and EI opens EI to humans, providing a fully operational and comprehensive environment. The main features of the infrastructure are (i) the causal connection between Virtual Worlds and Electronic Institutions, (ii) the automatic generation and update of the VIs' 3D visualization and (iii) the simultaneous participation of users from different virtual world platforms. We illustrate the execution of VIXEE system in a simple eAuction house example and use this example to evaluate the performance of our solution.     

Towards probabilistic footy tipping: a hybrid approach utilising genetically defined neural networks and linear programming

Using readily available data from the 1992–1995 Australian Football League season, we have developed a model that will readily predict the winner of a game, together with the probability of that win. This model has been developed using a genetically modified neural network to calculate the likely winner, combined with a linear program optimisation to determine the probability of that occurring in the context of the tipping competition scoring regime. This model has then been tested against 484 tippers in a probabilistic tipping competition for the 2002 season. We have found that the performance of the combined neural network, linear program model compared most favorably with other model based tipping programs and human tippers.     

Context‐Centric Speech‐Based Human–Computer Interaction

This paper describes research that addresses the problem of dialog management from a strong, context‐centric approach. We further present a quantitative method of measuring the importance of contextual cues when dealing with speech‐based human–computer interactions. It is generally accepted that using context in conjunction with a human input, such as spoken speech, enhances a machine's understanding of the user's intent as a means to pinpoint an adequate reaction. For this work, however, we present a context‐centric approach in which the use of context is the primary basis for understanding and not merely an auxiliary process. We employ an embodied conversation agent that facilitates the seamless engagement of a speech‐based information‐deployment entity by its human end user. This dialog manager emphasizes the use of context to drive its mixed‐initiative discourse model. A typical, modern automatic speech recognizer (ASR) was incorporated to handle the speech‐to‐text translations. As is the nature of these ASR systems, the recognition rate is consistently less than perfect, thus emphasizing the need for contextual assistance. The dialog system was encapsulated into a speech‐based embodied conversation agent platform for prototyping and testing purposes. Experiments were performed to evaluate the robustness of its performance, namely through measures of naturalness and usefulness, with respect to the emphasized use of context. The contribution of this work is to provide empirical evidence of the importance of conversational context in speech‐based human–computer interaction using a field‐tested context‐centric dialog manager.     

Game‐Based Simulation for Cross‐Cultural Decision Making Training

Increasing globalization has had a major impact on manufacturing and service industries as well as on coalition operations conducted by the military. What is common to both the commercial and military sectors is the recent surge in interest in cross‐cultural decision making (CCDM) training. Existing CCDM training approaches tend to employ either some form of multi‐agent simulation or some variant of classical game theory. Despite their manifest benefits, these approaches have specific limitations that need to be overcome to create an effective cross‐cultural training system. Multi‐agent simulations typically lack theoretical underpinnings while classical game theory‐based approaches take a limited view of strategic decision making. Specifically, by adopting a Western view of rationality, game‐theoretic approaches fail to accommodate considerations such as fairness, altruism and reciprocity. Empirical research in strategic economic games has shown that humans respond to more than merely monetary incentives. In particular, research has shown that cultural norms play a central role in human decision making behavior, especially in non‐Western cultures. This paper presents an innovative approach to game‐based simulation that combines findings from behavioral game theory with classical game theory and multi‐agent simulation to exploit the strengths of each approach while making learning enjoyable, memorable, and fun. An illustrative game‐based simulation for CCDM training is also presented. The simulation framework is equally applicable to teaching other soft skills as well as skills that are too hazardous or too expensive to teach in the realworld through live exercises. © 2012 Wiley Periodicals, Inc.     

The effect of violent and non-violent computer games on cognitive performance

Two studies were conducted in order to determine the impact computer games had on the cognitive performance. Study 1 evaluated a measure of cognition, which incorporates aspects of short-term working memory, visual attention, mathematical decision making, and auditory perception. Study 2 measured the cognitive performance between those who did not play video games versus those who played either a violent or non-violent video game. Results from Study 1 indicate participants needed approximately four trials to reach asymptotic performance on the cognitive measure. Results of Study 2 showed that participants who did not play any video game did not have a change in their cognitive performance, while those who played either a violent or non-violent video game had an increase in their cognitive performance.