AI Social Agents

表情游戏正在开发一个社交网络平台，人们可以在这个平台上玩游戏。人工智能(AI)成员的存在加强了这类网络，人工智能成员可以指导人类成员执行某些活动、提供教程支持、建立小组等。最先进的AI社交代理相当平淡无奇-他们是被动的，而不是主动的，他们往往以脚本化和大部分乏味的方式做出反应。公平地说，这些代理人本质上被视为功利性的，与之互动并不是特别有趣。因此，Eemote Games有很大的改进空间，要成为市场领导者，Eemote Games希望他们的人工智能代理为社会增加更大的价值。然而，构建智能代理以智能(也许是适度创造性的)，是一个非同小可的问题，它将依赖于各种人工智能方法的使用，包括约束求解、多代理系统、机器学习、规划和自然语言处理。Emote将迭代地为他们的网络构建越来越智能的AI代理，具有从游戏到游戏的转移功能。每个新版本的代理都将执行更多任务，并以更智能的方式执行现有任务。为了做到这一点，每个新版本都将通过系统的实验测试进行通知，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，这些系统能够提供本项目所需的那种多方面的智能行为。在顶层，我们将基于智能体行为的信念、愿望和意图(BDI)模型构建多智能体系统体系结构。BDI代理非常适合于嵌入、社交、被动和目标定向的情况。这与我们的要求部分匹配，我们将试验各种扩展，这些扩展可能更适合于更主动的情况。我们打算确定最佳的通信和行为框架来控制网络中代理的交互。每个代理都将是半自治的，并且像任何其他网络成员一样，有关于游戏上下文的任务要执行。我们将尝试各种计划方法来控制任务的整体执行，这将涉及到呼吁各种人工智能方法。例如，人工智能代理可能被要求向合适的网络成员广播一条新闻，接收网络成员的反馈，并学习在未来更好地完成同样的任务。在总体行动计划中，将使用约束满足技术来确定要通知网络的哪些成员。新闻的传播和反馈意见的解析将涉及自然语言处理技术。最后，给出网络成员的正反馈和负反馈，智能体将建立一个机器学习问题来生成一个分类器，将来可以用来改进基于约束的搜索，以供网络成员交流新闻。Emote Games将构建整个社交网络和其中的AI代理。他们将指定代理将从事的活动的类型，并编写简单的静态脚本来完成这一任务。帝国理工学院将负责提出规划方法和人工智能技术，以更可信、更智能、更有吸引力的方式完成任务。此外，帝国理工学院将提供实验结果，证明哪种方法最适合特定任务。为了进行这类实验，我们计划在受控情况下工作，在这种情况下，我们从受试者那里获得他们对与人工智能代理交互的满意度的反馈。在代理的性能不是最优的情况下，帝国理工学院的研究人员将研究新的人工智能方法来改善情况。

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.