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。     

具有可信度的主体信念

本文提出了一种基于可信度逻辑的主体信念程度描述方法，这种方法通过定义信念的可信度描述主体信念的不确定性，允许相互矛盾的主体信念同时存在，并给出了可信度的计算方法与推理规则。信念修正和更新时，根据主体不同的性格采取不同的选择倾向，本文提出的“必要时修正”方法具有很高的效率和实用性。本文选择基于区间的时间逻逻辑描述主体信念中的时间概念，为涉及到大量时间段行为与操作的应用提供了一个新的信念逻辑描述方法。     

Agent Programming in 3APL

An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.To support this new style of programming, we propose the agent programming language 3APL. 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative and logic programming. From imperative programming the language inherits the full range of regular programming constructs, including recursive procedures, and a notion of state-based computation. States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. These features are well-understood and provide a solid basis for a structured agent programming language. Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.Applying the metaphor of intelligent agents means taking a design stance. From this perspective, a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. We illustrate how the metaphor of intelligent agents is supported by the programming language. We also discuss the design of control structures for rule-based agent languages. A control structure provides a solution to the problem of which goals and which rules an agent should select. We provide a concrete and intuitive ordering on the practical reasoning rules on which such a selection mechanism can be based. The ordering is based on the metaphor of intelligent agents. Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.     

时间上下文的属性限制及一致性分析研究*

首先分析了本体创建的一般过程,定义了一组时间关系的表示函数及通用的时间限制性公理;在此基础上设计一个面向带时间特征的概念属性逻辑关系一致性分析算法;最后,直接针对本体概念的时间属性,设计了一个时间属性值描述的一致性分析算法。实际应用表明,提出的两个算法,既能有效地实现带时间侧面的本体属性的一致性检查,又能针对时间属性本身消除其不一致性。上述算法可应用于带时间上下文的本体创建中分析并检查其一致性。     

基于本体的城市交通的知识分析和推理

以城市交通为研究和应用背景,基于本体模型的知识表示技术,建立城市交通本体。对城市交通领域知识进行规范描述,详细描述了定义类层次及类的属性和关系,以及实例的基本规则,并从领域知识中提取出类内公理和类间公理,建立公理库,用于领域知识的一致性分析和知识推理。解决了语义层次上信息共享和交互的问题,为上海城市交通信息网格的交通信息服务提供语义支撑。     

Relevance sensitive belief structures

We propose a new relevance sensitive model for representing and revising belief structures, which relies on a notion of partial language splitting and tolerates some amount of inconsistency while retaining classical logic. The model preserves an agent's ability to answer queries in a coherent way using Belnap's four‐valued logic. Axioms analogous to the AGM axioms hold for this new model. The distinction between implicit and explicit beliefs is represented and psychologically plausible, computationally tractable procedures for query answering and belief base revision are obtained. This revised version was published online in June 2006 with corrections to the Cover Date.     

Abductive logic programming agents with destructive databases

In this paper we present an agent language that combines agent functionality with a state transition theory and model-theoretic semantics. The language is based on abductive logic programming (ALP), but employs a simplified state-free syntax, with an operational semantics that uses destructive updates to manipulate a database, which represents the current state of the environment. The language builds upon the ALP combination of logic programs, to represent an agent??s beliefs, and integrity constraints, to represent the agent??s goals. Logic programs are used to define macro-actions, intensional predicates, and plans to reduce goals to sub-goals including actions. Integrity constraints are used to represent reactive rules, which are triggered by the current state of the database and recent agent actions and external events. The execution of actions and the assimilation of observations generate a sequence of database states. In the case of the successful solution of all goals, this sequence, taken as a whole, determines a model that makes the agent??s goals and beliefs all true.     

Maximal Introspection of Agents

This paper concerns the representation of introspective belief and knowledge in multi-agent systems. An introspective agent is an agent that has the ability to refer to itself and reason about its own beliefs. It is well-known that representing introspective beliefs is theoretically very problematic. An agent which is given strong introspective abilities is most likely to have inconsistent beliefs, since it can use introspection to express self-referential beliefs that are paradoxical in the same way as the classical paradoxes of self-reference. In multi-agent systems these paradoxical beliefs can even be expressed as beliefs about the correctness and completeness of other agents' beliefs, i.e., even without the presence of explicit introspection. In this paper we explore the maximal sets of introspective beliefs that an agent can consistently obtain and retain when situated in a dynamic environment, and when treating beliefs “syntactically” (that is, formalizing beliefs as axioms of first-order predicate logic rather than using modal formalisms). We generalize some previous results by Perlis [1985] and des Rivières & Levesque [1988].     

Belief,awareness, and limited reasoning

Several new logics for belief and knowledge are introduced and studied, all of which have the property that agents are not logically omniscient. In particular, in these logics, the set of beliefs of an agent does not necessarily contain all valid formulas. Thus, these logics are more suitable than traditional logics for modelling beliefs of humans (or machines) with limited reasoning capabilities. Our first logic is essentially an extension of Levesque's logic of implicit and explicit belief, where we extend to allow multiple agents and higher-level belief (i.e., beliefs about beliefs). Our second logic deals explicitly with “awareness,” where, roughly speaking, it is necessary to be aware of a concept before one can have beliefs about it. Our third logic gives a model of “local reasoning,” where an agent is viewed as a “society of minds,” each with its own cluster of beliefs, which may contradict each other.     

Ontology oriented programming in go!

In this paper we introduce the knowledge representation features of a new multi-paradigm programming language called go! that cleanly integrates logic, functional, object oriented and imperative programming styles. Borrowing from L&O [1] go! allows knowledge to be represented as a set of labeled theories incrementally constructed using multiple-inheritance. The theory label is a constructor for instances of the class. The instances are go!’s objects. A go! theory structure can be used to characterize any knowledge domain. In particular, it can be used to describe classes of things, such as people, students, etc., their subclass relationships and characteristics of their key properties. That is, it can be used to represent an ontology. For each ontology class we give a type definition—we declare what properties, with what value type, instances of the class have—and we give a labeled theory that defines these properties. Subclass relationships are reflected using both type and theory inheritance rules. Following [2], we shall call this ontology oriented programming. This paper describes the go! language and its use for ontology oriented programming, comparing its expressiveness with Owl, particularly Owl Lite[3]. The paper assumes some familiarity with ontology specification using Owl like languages and with logic and object oriented programming.     

Belief revision — an axiomatic approach

Belief revision systems aim at keeping a database consistent. They mostly concentrate on how to record and maintain dependencies. We propose an axiomatic system, called MFOT, as a solution to the problem of belief revision. MFOT has a set of proper axioms which selects a set of most plausible and consistent input beliefs. The proposed nonmonotonic inference rule further maintains consistency while generating the consequences of input beliefs. It also permits multiple property inheritance with exceptions. We have also examined some important properties of the proposed axiomatic system. We also propose a belief revision model that is object-centered. The relevance of such a model in maintaining the beliefs of a physician is examined.     

Local closed world reasoning with description logics under the well-founded semantics

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.     

A Methodology to Create Legal Ontologies in a Logic Programming Based Web Information Retrieval System

Web legal information retrieval systems need the capability to reason with the knowledge modeled by legal ontologies. Using this knowledge it is possible to represent and to make inferences about the semantic content of legal documents. In this paper a methodology for applying NLP techniques to automatically create a legal ontology is proposed. The ontology is defined in the OWL semantic web language and it is used in a logic programming framework, EVOLP+ISCO, to allow users to query the semantic content of the documents. ISCO allows an easy and efficient integration of declarative, object-oriented and constraint-based programming techniques with the capability to create connections with external databases. EVOLP is a dynamic logic programming framework allowing the definition of rules for actions and events. An application of the proposed methodology to the legal web information retrieval system of the Portuguese Attorney General’s Office is described.     

Reasoning about knowledge and belief: a survey

We examine a number of logics of knowledge and belief from the perspective of knowledge-based systems. We are concerned with the beliefs of a knowledge-based system, including both the system's base set of beliefs–those garnered directly from the world–and beliefs that follow from the base set. Three things to consider with such logics are the expressive power of the language of the logic, the correctness and completeness of the inferences sanctioned, and the speed with which it is possible to determine whether a given sentence is believed. The influential possible worlds approach to representing belief has the property of logical omniscience, which makes for inferences that are unacceptable in the context of belief and may take too much time to make. We examine a number of weak logics which attempt to deal with these problems. These logics divide into three categories: those that admit incomplete or inconsistent situations into their semantics, those that posit a number of distinct states for a believer which correspond roughly to frames of mind, and those that incorporate axioms or other syntactic entities directly into the semantics. As to expressive power, we consider whether belief should be represented by a predicate or a sentential operator and examine the boundary between self-referential and inconsistent systems. Finally, we consider logics of believing only , which add the assumption that a system's base set of beliefs are, in a certain sense, all that it believes.     

Reasoning about knowledge,belief and certainty in hierarchical multi-agent systems

Multi-agent systems (MAS) have received extensive studies in the last decade. However, little attention is paid to investigation on reasoning about logics in MAS with hierarchical structures. This paper proposes a complete quantified temporal KBC (knowledge, belief and certainty) logic and corresponding reasoning in hierarchical multi-agent systems (HMAS). The key point is that internal beliefs and certainty, and external belief and certainty are considered in our logic. The internal beliefs and certainty show every agent is autonomous, while the external belief and certainty indicate the mutual influence of mental attitudes between two different agents on different layers in HMAS. To interpret this logic, we propose four classes of corresponding quantified interpreted systems, and define first-order KBC axiomatisations over HMAS, which are sound and complete with respect to the corresponding semantical classes. Finally, we give a case study to show the advantages in terms of expressiveness of our logic.     

Using SPIN for verification of multiagent data analysis

The paper presents an approach to formal verification of multiagent data analysis algorithms for ontology population. The system agents correspond to information items of the input data and the rule of ontology population and data processing. They determine values of information objects obtained at the preliminary phase of the analysis. The agents working in parallel check the syntactic and semantic consistency of tuples of information items. Since the agents operate in parallel, it is necessary to verify some important properties of the system related to it, such as the property that the controller agent correctly determines the system termination. In our approach, the SPIN model checking tool is used. The agent protocols are written in the Promela language (the input language of the tool), and the properties of the multiagent data analysis system are expressed in liner time logic (LTL). We carried out several experiments to check this model in various modes of the tool and for various numbers of agents.     

An experimental logic based on the fundamental deduction principle

Experimental logic can be viewed as a branch of logic dealing with the actual construction of useful deductive systems and their application to various scientific disciplines. In this paper we describe an experimental deductive system called the SYMbolic EVALuator (i.e. SYMEVAL) which is based on a rather simple, yet startling principle about deduction, namely that deduction is fundamentally a process of replacing expressions by logically equivalent expressions. This principle applies both to logical and domain-dependent axioms and rules. Unlike more well-known logical inference systems which do not satisfy this principle, herein is described a system of logical axioms and rules called the SYMMETRIC LOGIC which is based on this principle. Evidence for this principle is given by proving theorems and performing deduction in the areas of set theory, logic programming, natural language analysis, program verification, automatic complexity analysis, and inductive reasoning.     

Constructive belief and rational representation

It is commonplace in artificial intelligence to divide an agent's explicit beliefs into two parts: the beliefs explicitly represented or manifest in memory, and the implicitly represented or constructive beliefs that are repeatedly reconstructed when needed rather than memorized. Many theories of knowledge view the relation between manifest and constructive beliefs as a logical relation, with the manifest beliefs representing the constructive beliefs through a logic of belief. This view, however, limits the ability of a theory to treat incomplete or inconsistent sets of beliefs in useful ways. We argue that a more illuminating view is that belief is the result of rational representation. In this theory, the agent obtains its constructive beliefs by using its manifest beliefs and preferences to rationally (in the sense of decision theory) choose the most useful conclusions indicated by the manifest beliefs.     

ORM模型转换为OWL 2公理的映射规则

对象角色建模方法ORM目前已发展成为一种本体工程方法。需要将ORM表达的领域知识发布到语义Web上,以供不同应用系统共享和交换。OWL 2是W3C推荐使用的语义Web本体语言。探讨了将ORM模型映射为OWL 2公理的方法,通过模型等价变换和引入新的公理,给出了所有可行的模型映射规则。