面向智能主体的程序设计

面向智能主体的程序设计是一种以计算的社会观为基础的新型程序设计范例。本文介绍我们在ＡＯＰ方面进行的研究工作。ＡＯＰ工作以多个智能主体的知识信息处理问题为背景，采用ＡＯＰ的语法途径，强调将知识本文首先讨论与ＡＯＰ相关的智能主体的体系结构。然后，以ＡＯＰ语言ＲＯＯＴ的技术支点（即面向对象程序设计技术、基于条件重写的逻辑程序设计技术和元级推理技术等）为线索介绍ＲＯＯＴ。最后通过一个简单例说明ＲＯＯＴ的Ａ     

铁路选线优化设计专家系统的设计

铁路选线设计专家系统（ＲＯＤＥＳ）是我们用ＬＩＳＰ、Ｃ＾＋＋、ＦＯＲＴＲＡＮ语言和ＡＵＴＯＣＡＤ实现的一个实际专家辅助设计系统。本文介绍ＲＯＤＥＳ的基本设计思想，讨论它的知识表达、推理机制及解题过程。     

概念代数—新一代数据库系统的理论

念代数ＣＡ是Ｎｉｌｓｓｏｎ教授以格（ｌａｔｔｉｃｅ）理论为数学背景提出的一个新的代数系统。在ＣＡ中，关系范型、ＯＯ范型、逻辑程序设计和框架知识表示获得了统一的表示－概念项，项进一步组成句子。一个完整的知识库就由这样折的项和句子组成。推理操作是一组被称为重写规则的代数公理。目前的ＣＡ还只是个代数系统雏形，有很多方面有待扩充。本文对概念代数进行综述，并给出初步的扩充。     

情感等级BDI主体模型研究

信念、愿望和意图(BDI)模型是近年来影响最为深远的主体技术之一。文中把命题动态逻辑和无穷值的ukasiewicz逻辑进行融合后对情感等级BDI主体模型进行了形式化。为通过信念度、愿望度、意图度、害怕度、焦虑度和自信度对不确定性行为进行表示和推理,把相应的公理添加到ukasiewicz逻辑中。文中的情感等级BDI主体模型的行为是通过添加具体条件的每种背景的不同测度来决定,清晰地表示主体的心理状态和情感状态的不确定性。文中对情感等级BDI模型进行公理化,并说明它们对主体行为的影响。此模型可较轻易地向包括其它心理状态和情感状态的主体进行推广。文中在给出情感等级BDI模型的语言、语义及公理和演绎规则后,证明此逻辑系统的可靠性和完全性。随后给出情感等级BDI主体模型的不同背景之间的相互关系,并对该主体的买房行动进行实例分析。本研究立足于不确定性的表示和推理,旨在为分布式人工智能提供形式支持。     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一种基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性能逻辑及模糊集合理论作为其逻辑基础，并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性逻辑及模糊集合理论作为其逻辑基础并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。     

实时专家智能控制系统REICS设计与应用

本文研究并设计了一种新型的用于工业控制过程的实时专家智能控制系统（ＲＥＩＣＳ），提出了一种表达控制领域知识的广义产生式规则，解决了多级知识表示和多级推理控制策略的问题。本文还介绍了ＲＥＩＣＳ设计过程、实现的方法和技术。通过温度控制以及在直流电机调速控制方面的应用研究表明，ＲＥＩＣＳ较常规ＰＩＤ和Ｆｕｚｚｙ控制有明显优越性，可适用于大型、复杂和不确定性系统的智能控制。     

用“规则架＋规则体”表示方法研制医疗专家系统

产生式是专家系统中常用的知识表示方法，但是这种表示方法存在两个问题：一是处理计算的能力较弱，二是当规则比较多时，推理的速度明显下降。在新生儿疾病诊断咨询专家系统（ＸＲＫ）中，采用了我国学者提出的“规则果＋规则体”的表示方法，该法把规则分为两个层次，规则架层描述推理网络中各结．点之间的层次关系，规则体层用以描述结点的具体定值方法，由于节点的定值与推理网络的分离，增强了系统的计算能力，使可信度的计算变得方便灵活。为了提高推理速度，我们用每一个规则体前提条件总数的最小值作为检索的条件，从大量规则中筛选一个子集，并提供了正向推理、反向推理及正反向结合的综合推理策略。ＸＲＫ系统建立了与ＦＯＸＢＡＳＥ的接口，原始数据可直接从病案管理系统提取，实现了管理信息系统与专家系统的结合，使病案管理智能化。为了便于专家修改和扩充知识库内容，ＸＲＫ系统还设计了一个交互式知识获取模块，该模块能对知识库进行增、删、改等操作，实现知识库的管理功能。     

多任务航天器平台的姿态和轨道控制系统

由ＣＮＥＳ（法国航天局）倡议的法国通用卫星平台ＰＲＯＴＥＵＳ定义为一个廉价小卫星，它运行在近地轨道，无需进行重大的具体个性就能够执行多种任务。所考虑的不同种类任务是：惯性定向或对太阳定向，对地球定向（对天顶定向或对天底定向）。ＣＮＥＳ已选定ＡＥＲＯＳＰＡＴＩＡＬＥ负责ＰＲＯＴＥＵＳ平台的研制。本文先综述关键要求，然后描述现已开发的通用平台的姿态和轨道控制系统（ＡＯＣＳ）。ＡＥＲＯＳＰＡＴＩＡＬＥ合     

基于m类逻辑的模式分类方法及其硬件实现

基于ｍ类逻辑的模式分类方法及其硬件实现张自力（西南师范大学计算机科学系重庆６３０７１５）ＴＨＥｍ－ＣＬＡＳＳＬＯＧＩＣＢＡＳＥＤＰＡＴＴＥＲＮＣＬＡＳＳＩＦＩＣＡＴＩＯＮＡＮＤＩＴＳＨＡＲＤＷＡＲＥＩＭＰＬＥＭＥＮＴＡＴＩＯＮ￥ＺｈａｎｇＺｉｌｉ（Ｄ...     

RAO logic for multiagent framework

In this paper,we deal with how agents reason about knowledge of others in multiagent system.We first present a knowledge representation framework called reasoning about others(RAO) which is designed specifically to represent concepts and rules used in reasoning about knowledge of others.From a class of sentences usually taken by people in daily life to reason about others,a rule called position exchange principle(PEP)is abstracted.PEP is described as an axiom scheme in RAO and regarded as a basic rule for agents to reason about others,and further it has the similar form and role to modus ponens and(K) axion of knowledge logic.The relationship between speech acts and common sense is also discussed which is necessary for RAO.Based on ideas from situation calculus,this relationship is characterized by an axiom schema in RAO.Our theories are also demonstrated by an example.     

换位原理与几个模态特征公式的有效性

采用换位原理的推理规则能够使得多Agent系统中关于其它Agent的状况和行为的推理变得简明和清晰。本文探讨了几个正规模态特征公式的有效性与框架性质之间的关系，发现一些直观上成立的模态公式也是有条件成立的，并从模态逻辑和Kripke可能世界语义的角度给出了文[1～3]中换位原理（PEP）规则有效性的语义证明。     

A Fuzzy Inference Network Model for Search Strategy Using Neural Logic Network

In this paper, a fuzzy inference network model for search strategy using neural logic network is presented. The model describes search strategy, and neural logic network is used to search. Fuzzy logic can bring about appropriate inference results by ignoring some information in the reasoning process. Neural logic networks are powerful tools for the reasoning process but not appropriate for the logical reasoning. To model human knowledge, besides the reasoning process capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct a fuzzy inference network model based on the neural logic network, extending the existing rule inference network. And the traditional propagation rule is modified.     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

The entailment principle for dempster—shafer granules

We discuss the rule of inference called the entailment principle which plays a significant role in the possibilistic type reasoning used in the theory of approximate reasoning. We extend this principle to situations in which the knowledge is a type of combination of possibilistic and probabilistic information which we call Dempster—Shafer granules. We discuss the conjunction of these D—S granules and show that Dempster's rule of combination is a special application of conjunction followed by a particular implementation of the entailment principle.     

Learning strategies for task delegation in norm-governed environments

How do I choose whom to delegate a task to? This is an important question for an autonomous agent collaborating with others to solve a problem. Were similar proposals accepted from similar agents in similar circumstances? What arguments were most convincing? What are the costs incurred in putting certain arguments forward? Can I exploit domain knowledge to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues and domain knowledge, and where these models are used to guide future delegation decisions. Our approach combines ontological reasoning, argumentation and machine learning in a novel way, which exploits decision theory for guiding argumentation strategies. Using our approach, intelligent agents can autonomously reason about the restrictions (e.g., policies/norms) that others are operating with, and make informed decisions about whom to delegate a task to. In a set of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that decision-theory, machine learning and ontology reasoning techniques can significantly improve dialogical outcomes.     

A model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets

In many application areas there is a need to represent human-like knowledge related to spatio-temporal relations among multiple moving objects. This type of knowledge is usually imprecise, vague and fuzzy, while the reasoning about spatio-temporal relations is intuitive. In this paper we present a model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets. The model should be suitable for the design of a knowledge base for real-time, multi-agent-based intelligent systems that include expert or user human-like knowledge. The central part of the model is the knowledge representation scheme called FuSpaT, which supports the representation and reasoning for domains that include imprecise and fuzzy spatial, temporal and spatio-temporal relationships. The scheme is based on the high-level Petri nets called Petri nets with fuzzy spatio-temporal tokens (PeNeFuST). The FuSpaT scheme integrates the theory of the PeNeFuST and 117 spatio-temporal relations.The reasoning in the proposed model is a spatio-temporal data-driven process based on the dynamical properties of the scheme, i.e., the execution of the Petri nets with fuzzy spatio-temporal tokens. An illustrative example of the spatio-temporal reasoning for two agents in a simplified robot-soccer scene is given.     

A rule based knowledge transaction model for mobile environments

In this paper, we propose and formalize a rule based knowledge transaction model for mobile environments. Our model integrates the features of both mobile environments and intelligent agents. We use logic programming as a mathematic tool and formal specification method to study knowledge transaction in mobile environments. Our knowledge transaction model has the following major advantages: (1) It can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments. (2) It is knowledge oriented and has a declarative semantics inherited from logic programming. (3) It is a formalization that can be applied to general problem domains. We show that our model can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments.     

IMPROVING THE SCOPE OF INTELLIGENT TUTORING BY ADAPTING A CASE-BASED METHODOLOGY THROUGH A DISTRIBUTED ARCHITECTURE

This paper describes an architecture for distributed case-based tutoring, called DICABTU, which provides an environment that facilitates cooperation among independent agents working together to provide highly individualized instruction. The fusion of these agents through a blackboard platform creates a distributed learning environment in which the most competent agents are called up to assist a student during a tutoring session. Following a curriculum derived from a node-based knowledge network, case-based reasoning is used to compose lessons at various levels of knowledge, to generate teaching materials, and to solve problems.