AI Social Agents

游戏公司正在开发一个社交网络平台，在这个平台上，游戏将由一个网络的人玩。人工智能（AI）成员的存在增强了这种网络，它可以指导人类成员执行某些活动，提供教程支持，建立子组等。最先进的AI社交代理相当不起眼-他们是被动的而不是主动的，他们倾向于以脚本和很大程度上不有趣的方式做出反应。公平地说，这样的代理人被视为功利的性质，并不是特别有趣的互动。因此，还有很大的改进空间，为了迎合市场领导者，Zynte Games希望他们的人工智能代理为社会增加更大的价值。然而，构建智能体（也许是适度的创造性）是一个不平凡的问题，它将依赖于各种人工智能方法的使用，包括约束求解，多智能体系统，机器学习，规划和自然语言处理。Cortete将迭代地为他们的网络构建越来越智能的人工智能代理，其功能可以从游戏转移到游戏。代理的每个新版本将执行更多的任务，并以更智能的方式执行现有任务。为了做到这一点，每一个新的版本将通过系统的实验测试，这将在帝国理工学院进行。帝国理工学院的联合推理小组（www.doc.ic.ac.uk/crg）在构建集成系统方面拥有丰富的经验，这些系统提供了该项目所需的多方面智能行为。在顶层，我们将建立一个基于信念，愿望和意图（BDI）模型的Agent行为的多Agent系统架构。BDI智能体非常适合嵌入式、社会性、反应性和目标导向的情况。这是一个部分匹配我们的要求，我们将尝试各种扩展，这可能是更适合formore主动的情况。我们打算确定最好的通信和行为框架来控制网络中的代理的交互，每个代理将是半自治的，并且将像任何其他网络成员一样，有任务要执行。我们将尝试各种规划方法来控制任务的整体执行，这将涉及到吸引各种AI方法。例如，AI代理可能需要向正确的网络成员广播一条新闻，从网络成员那里接收反馈，并学习在未来更好地完成同样的任务。在一个整体的行动计划中，确定哪些网络成员要通知将使用约束满足技术来解决。新闻的传播和反馈评论的解析将涉及自然语言处理技术。最后，给出网络成员的积极和消极反馈，代理将建立一个机器学习问题，以生成一个分类器，该分类器可用于将来改进基于约束的搜索网络成员以向其传递新闻。Applete Games将构建整个社交网络和其中的AI代理。他们将指定代理将进行的活动类型，并编写简单的静态脚本来完成这一任务。帝国将负责提出规划方法和人工智能技术，以更可信、更智能和更明智的方式执行任务。此外，帝国将提供实验结果，证明哪种方法最适合特定任务。为了进行这类实验，我们计划在受控的情况下工作，我们从受试者那里获得关于他们与AI代理互动的满意度的反馈。如果代理的性能不佳，Imperial研究人员将研究新的人工智能方法来改善问题。

Emote Games are developing a social networking platform upon whichgames will be played by a network of people. Such networks areenhanced by the presence of Artificial Intelligence (AI) members,which can direct human members to perform certain activities, offertutorial support, set up subgroups, etc. State of the art AI socialagents are fairly unimpressive - they are reactive rather thanproactive, and they tend to react in scripted and largelyuninteresting ways. It is fair to say that such agents are viewed asutilitarian in nature and not particularly interesting to interactwith. There is therefore much room for improvement, and to becomemarket leaders, Emote Games want their AI agents to add greater valueto the society. However, building agents to act intelligently (andperhaps moderately creatively), is a non-trivial problem which willrely on the use of various AI methods, including constraint solving,multi-agent systems, machine learning, planning and natural languageprocessing.Emote will iteratively build increasingly smarter AI agents for theirnetwork, with functionality that transfers from game to game. Each newrelease of the agents will perform more tasks, and perform existingtasks in more intelligent ways. To do this, each new release will beinformed by systematic experimental testing, which will be carried outat Imperial College. The Combined Reasoning Group at Imperial(www.doc.ic.ac.uk/crg) has much experience of building integratedsystems that deliver the kind of multifaceted intelligent behaviourrequired for this project. At the top level, we will build amulti-agent system architecture based on the beliefs, desires andintentions (BDI) model of agent behaviour. BDI agents are ideallysuited for situations where they are embedded, social, reactive, andgoal directed. This is a partial match to our requirements, and wewill experiment with various extensions which may be more suitable formore pro-active situations. We intend to determine the bestcommunication and behaviour framework to control the interaction ofthe agents within the network.Each agent will be semi-autonomous and will, like any other networkmember, have tasks to perform with respect to the game context. Wewill experiment with various planning approaches to control theoverall execution of the task, which will involve appealing to variousAI methods. As an example, an AI agent might be required to broadcasta piece of news to the right kind of network members, receive feedbackfrom the network members, and learn to do the same kind of task betterin future. Within an overall plan of action, determining which membersof the network to inform will be solved using constraint satisfactiontechniques. The communication of the news and the parsing of feedbackcomments will involve natural language processing techniques. Finally,given positive and negative feedback from the network members, theagent will set up a machine learning problem to generate a classifierwhich can be used in future to refine the constraint-based search fornetwork members to communicate news to.Emote Games will build the overall social network and the AI agents in it. They will specify the kinds of activities that agents will undertake, and write simple static scripts to do this. Imperial will beresponsible for suggesting planning methods and AI techniques toundertake the tasks in a more believable, intelligent and engagingfashion. Moreover, Imperial will deliver experimental results whichdemonstrate which approach(es) are the best for particular tasks. Toperform these kinds of experiments, we plan to work in controlledsituations where we gain feedback from subjects about theirsatisfaction with their interactions with the AI agents. In caseswhere the performance of the agents is sub-optimal, Imperialresearchers will research novel AI methods to improve matters.