基于流演算的智能虚拟人模型研究与实现*

在研究流演算理论及其实现语言FLUX的基础上,将流演算与虚拟现实技术中的虚拟人相结合,提出了一个基于目标驱动的、有自主行动能力的虚拟人模型。设计了动作检测模块,同时使用了动作队列,根据动作检测的结果来决定是否执行下一个动作,使虚拟人可以针对动态变化的虚拟环境进行有效的行动规划。利用此模型可以快速构建出一个在不完全可知的虚拟环境中通过感知到的有限信息进行实时的、自主行动推理的智能虚拟人。最后,实现了办公室场景中智能虚拟人行动推理系统。     

基于流演算的智能体行动推理模型设计与实现

并发约束程序设计在人工智能程序设计领域中占据越来越重要的位置,约束处理规则作为新一代的并发程序设计正倍受关注.对约束处理规则和流演算理论及其实现语言FLUX进行了研究,结合流演算和JCHR推理模型优点,设计了一种基于Java的流演算解释器JFLUX,同时提出了一个基于目标驱动的,在不完全可知的虚拟环境中通过感知到的有限信息进行自主行动推理能力的智能体模型,实现了办公室场景中智能体行动推理系统.     

独立于设计者的行动推理

高水平的智能机器人要求能够独立地对环境进行感知并进行正确的行动推理.在情境演算行动理论中表示带有感知行动及知识的行动推理需要外部设计者为agent写出背景公理、感知结果及相应的知识变化,这是一种依赖于设计者的行动推理.情境演算行动理论被适当扩充,感知器的表示被添加到行动理论的形式语言中,并把agent新知识的产生建立在感知器的应用结果之上.扩充后的系统能够形式化地表示机器人对环境的感知并把感知结果转换为知识,还能进行独立于设计者的行动推理,同时让感知行动的"黑箱"过程清晰化.     

一种基于π t演算的安全协议建模方法

安全协议模型是安全协议分析与验证的基础,现有的建模方法中存在着一些缺点,如:建模复杂、重用性差等.为此提出了一种类型化的π演算:πt演算,并给出了相应类型推理规则和求值规则,πt演算的安全性也得到了证明.πt演算可以对安全协议、协议攻击者进行形式化建模.基于πt演算的安全协议模型及其建模过程使用NRL协议为例做出了说明.同时给出了攻击者模型,并证明了基于πt演算的安全协议攻击者模型与D-Y攻击者模型在行动能力上是一致的.这保证了基于πt演算的安全协议模型的验证结果的正确性.基于πt演算的建模方法能在协议数据语义、协议参与者知识方面实现细致的描述.与同类方法相比,该方法可提供多种分析支持,具有更好的易用性、重用性.分析表明,该方法可以在建模中发现一定的安全协议漏洞.     

行动推理中若干问题的研究

1 引言我们所面临的世界是不断动态变化的,一个智能系统往往需要对动态变化的环境做出反应,其中一个重要方面是对各种行动的结果进行预测、推理,以决定下一步的目标和动作。John McCarthy提出进行行动推理(Reasoning about action)研究,并认为行动推理在常识推理中占有基础性的地位。至此以后,行动推理成为人工智能的一项重要研究内容。利用形式化的方法对世界和行动进行描述和推理构成了行动推理的主要内容。行动推理有时也被称为行动逻辑。在这里我们把关于行动和变化的推理总称为行动推理。行动推     

基于赋时影响网的模拟退火与粒子群混合改进算法

赋时影响网是一种最近兴起的分析复杂系统关键事件行为及因果关系的建模范式,通过连接行动事件和期望效果来实现关系推理过程.基于赋时影响网设计算法在不确定条件下寻找有效行动方案和备选行动方案用以辅助系统分析决策.本文在标准粒子群优化算法的基础上给出了一种模拟退火改进策略:在规定的计算周期内以粒子群算法进行进化计算;同时为了避免陷入局部最优,采用模拟退火方法对所有粒子重新进行有选择的初始化,初始化之后再次应用粒子群算法.为了有效提高算法的运算速度,算法的实现应用了MPI.仿真结果表明,该算法具有较快的寻优能力和较好的鲁棒性.     

Pi演算与动态描述逻辑DDL的关系研究

分析了Pi演算与动态描述逻辑DDL之间的关系:DDL分别从静态结构与动态结构两方面对Pi演算的若干过程定义进行描述,体现了过程的逻辑结构及过程间逻辑关系的变化;以一种基于Pi演算的过程模型为基础,分析了ALC描述逻辑、TBox和ABox的语义,并通过一个例子说明Pi演算对DDL动态知识的变化过程的描述.以上工作表明:DDL的可判定推理可解决基于Pi演算的动态系统的某些一致性检测问题,而Pi演算对动态系统的描述能力可解决DDL动态知识的变化过程的描述问题.     

MAS交互协议的形式化描述和验证

在MAS的分析与设计中,Agent间的交互协议设计是极其重要的内容.特别是交互协议的正确性、有效性和可验证性尤为关键,因此形式化描述和验证多Agent的交互协议非常有必要.定义了用于描述多Agent基于对话的交互协议的一个演算,该演算是基于进程代数的并且独立于Agent的推理过程.该机制可以实现异构多Agent系统的交互协议验证.通过Agent会话环境的状态和演算的形式语义,可以验证会话协议的一些属性,例如:终止性、是否死锁等.该方法可以有效地解决交互协议的语义验证问题,因为协议的状态和Agent的行为由协议本身定义,并且可以避免基于状态搜索的状态空间爆炸问题.     

基于流演算和FLUX的办公室机器人控制

流演算是在经典情景演算的基础上发展起来的一种动作形式化描述理论,为人工智能领域的动作推理提供了强大的表示工具,在此基础上发展起来的逻辑程序设计语言FLUX,利用约束逻辑程序设计方法,具体地实现了动作推理.主要介绍流演算以及FLUX的基本知识,在此基础上对办公室机器人控制的实例进行了研究,并且利用FLUX语言实现了该实例,实验结果表明,流演算及其实现语言可以用来对机器人进行有效控制,具有良好的计算性能.     

基于区域延伸的定性空间表示

在区域连接演算(region connection calculus,RCC)理论基础上给出了区域延伸的形式定义.通过区域延伸,定义了关联空间的概念,进而提出了空间表示的一个模型,在这个模型中给出了空间中物体的空间拓扑关系、距离关系、方向关系以及位置等信息的定性表示.智能体对空间关系的确定是通过区域延伸实现的,模型为智能体在约束空间环境中的行动推理提供了一个新的表示方法.     

Event calculus and temporal action logics compared

We compare the event calculus and temporal action logics (TAL), two formalisms for reasoning about action and change. We prove that, if the formalisms are restricted to integer time, inertial fluents, and relational fluents, and if TAL action type specifications are restricted to definite reassignment of a single fluent, then the formalisms are not equivalent. We argue that equivalence cannot be restored by using more general TAL action type specifications. We prove however that, if the formalisms are further restricted to single-step actions, then they are logically equivalent.     

HYPOTHETICAL REASONING ABOUT ACTIONS: FROM SITUATION CALCULUS TO EVENT CALCULUS

Hypothetical reasoning about actions is the activity of preevaluating the effect of performing actions in a changing domain; this reasoning underlies applications of knowledge representation, such as planning and explanation generation. Action effects are often specified in the language of situation calculus, introduced by McCarthy and Hayes in 1969. More recently, the event calculus has been defined to describe actual actions, i.e., those that have occurred in the past, and their effects on the domain. Altough the two formalisms share the basic ontology of atomic actions and fluents, situation calculus cannot represent actual actions while event calculus cannot represent hypotethical actions. In this article, the language and the axioms of event calculus are extended to allow representing and reasoning about hypothetical actions, performed either at the present time or in the past, altough counterfactuals are not supported. Both event calculus and its extension are defined as logic programs so that theories are readily adaptable for Prolog query interpretation. For a reasonably large class of theories and queries, Prolog interpretation is shown to be sound and complete w.r.t. the main semantics for logic programs.     

事件本体中的动作推理研究

事件是随时间变化而变化的具体事实,事件是由动作、时间及其它要素组成,动作是事件定义中的主要构成部分.在面向事件的知识库系统中,关于动作的推理研究一直是重要的研究课题之一.现有的动作推理形式化系统旨在描述和推理现实世界中状态的变化,忽略了时间要素对推理过程的影响.针对这种不足,本文在描述逻辑的基础上扩充了一个Action-TBox和一个Action-ABox,并将事件本体中的动作要素和时间要素相结合,形式化定义了动作的一个三元组表示方式以及多种时间构造算子,用以刻画组合动作的发生过程,在此基础上研究了事件本体中关于动作的几种推理服务.     

Reasoning about Commitments in the Event Calculus: An Approach for Specifying and Executing Protocols

Commitments among agents are widely recognized as an important basis for organizing interactions in multiagent systems. We develop an approach for formally representing and reasoning about commitments in the event calculus. We apply and evaluate this approach in the context of protocols, which represent the interactions allowed among communicating agents. Protocols are essential in applications such as electronic commerce where it is necessary to constrain the behaviors of autonomous agents. Traditional approaches, which model protocols merely in terms of action sequences, limit the flexibility of the agents in executing the protocols. By contrast, by formally representing commitments, we can specify the content of the protocols through the agents' commitments to one another. In representing commitments in the event calculus, we formalize commitment operations and domain-independent reasoning rules as axioms to capture the evolution of commitments. We also provide a means to specify protocol-specific axioms through the agents' actions. These axioms enable agents to reason about their actions explicitly to flexibly accommodate the exceptions and opportunities that may arise at run time. This reasoning is implemented using an event calculus planner that helps determine flexible execution paths that respect the given protocol specifications.     

Merging deductive and inductive reasoning for processing textual descriptions of inter-human conflicts

We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive, and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This study demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. Suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.     

From event calculus to the scheduling problem. Semantics of action and temporal reasoning in aircraft maintenance

In this work we are interested in the logical and semantical aspects of reasoning about actions in a scheduling process. We present an adaptation of the event calculus of Kowalski and Sergot to the problem of determining the temporal structure of the operations that must be performed during the realization of some complex objectives. Our application domain is aircraft maintenance. We try to reason about the actions which are performed during an overhaul in order to help to schedule them. The original model reasons about changes, i.e. events which initiate or terminate propositions. The first step of this work was to improve the initial model by adding a temporal relation between events and propositions because in our field we also have to reason about events which only inform us about some propositions without affecting them. The second step of this work is to build a set of specific rules which temporally interpret the semantics of the usual specifications of the actions to be considered. This interpretation aims to associate each action with two events and some temporal relations which are usable by the general model. Temporal reasoning uses pertinent knowledge about the specific universe (here, the aircraft that we consider and the actions which may be performed on it). We outline a generative methodology to formalize this relevant knowledge efficiently. This cognitive approach brings more informational economy in temporal reasoning because only the relevant information is considered The temporal reasoning model and the methodology have been exemplified and tested on a complex part of an aircraft. In the future, adapted tools based on this approach will be developed, in order to solve several problems of aircraft maintenance scheduling.     

Synthesizing advanced transaction models using the situation calculus

The situation calculus is a versatile logic for reasoning about actions and formalizing dynamic domains. Using the non-Markovian action theories formulated in the situation calculus, one can specify and reason about the effects of database actions under the constraints of the classical, flat database transactions, which constitute the state of the art in database systems. Classical transactions are characterized by the so-called ACID properties. With non-Markovian action theories, one can also specify, reason about, and even synthesize various extensions of the flat transaction model, generally called advanced transaction models (ATMs). In this paper, we show how to use non-Markovian theories of the situation calculus to specify and reason about the properties of ATMs. In these theories, one may refer to past states other than the previous one. ATMs are expressed as such non-Markovian theories using the situation calculus. We illustrate our method by specifying (and sometimes reasoning about the properties of) several classical models and known ATMs.     

Iterated belief change in the situation calculus

John McCarthy's situation calculus has left an enduring mark on artificial intelligence research. This simple yet elegant formalism for modelling and reasoning about dynamic systems is still in common use more than forty years since it was first proposed. The ability to reason about action and change has long been considered a necessary component for any intelligent system. The situation calculus and its numerous extensions as well as the many competing proposals that it has inspired deal with this problem to some extent. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular, our approach is based on a well-developed theory of action in the situation calculus extended to deal with belief. Moreover, by augmenting this approach with a notion of plausibility over situations, our account handles nested belief, belief introspection, mistaken belief, and handles belief revision and belief update together with iterated belief change.