概率逻辑程序

1 引言最近几十年来,不确定性的管理在知识描述和推理中扮演着越来越重要的角色。为了处理不确定知识,人们提出了各种不同的形式化和方法论,其中大部分是直接或间接地基于概率论的。     

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.     

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.     

软件Agent的继承性研究

Agent作为一种受限的智能对象,对Agent的继承特性进行深入研究并将继承机制嵌入到AOP(agent-oriented programming)中则具有重要意义.文章基于BDI Agent模型,对软件Agent的继承性和复制行为进行了研究.从单继承和多继承两个方面给出了Agent继承的语义,将Agent实例的动态复制机制形式地划分为功能分割、逻辑分割、择优分割和返祖分割这4类,分析了每类分割方式的作用,并基于电子市场系统应用背景给出了相应的实例.     

Agents with emotions

This paper discusses the role of emotions in artificial agent design and implementation. The syntax and semantics of a simplified version of a logic‐based agent‐oriented programming language is presented. This programming language facilitates the implementation of artificial agents with emotions. Four types of emotions are distinguished: happiness, sadness, anger, and fear. These emotions are defined relative to agent's goals and plans. The emotions result from the agent's deliberation process and influence the deliberation process. The semantics of each emotion type is incorporated in the transition semantics of the presented agent‐oriented programming language. © 2010 Wiley Periodicals, Inc.     

Abduction in logic programming: A new definition and an abductive procedure based on rewriting

A long outstanding problem for abduction in logic programming has been on how minimality might be defined. Without minimality, an abductive procedure is often required to generate exponentially many subsumed explanations for a given observation. In this paper, we propose a new definition of abduction in logic programming where the set of minimal explanations can be viewed as a succinct representation of the set of all explanations. We then propose an abductive procedure where the problem of generating explanations is formalized as rewriting with confluent and terminating rewrite systems. We show that these rewrite systems are sound and complete under the partial stable model semantics, and sound and complete under the answer set semantics when the underlying program is so-called odd-loop free. We discuss an application of abduction in logic programming to a problem in reasoning about actions and provide some experimental results.     

Semantic forgetting in answer set programming

The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In this paper, we establish a declarative theory of forgetting for disjunctive logic programs under answer set semantics that is fully based on semantic grounds. The suitability of this theory is justified by a number of desirable properties. In particular, one of our results shows that our notion of forgetting can be entirely captured by classical forgetting. We present several algorithms for computing a representation of the result of forgetting, and provide a characterization of the computational complexity of reasoning from a logic program under forgetting. As applications of our approach, we present a fairly general framework for resolving conflicts in inconsistent knowledge bases that are represented by disjunctive logic programs, and we show how the semantics of inheritance logic programs and update logic programs from the literature can be characterized through forgetting. The basic idea of the conflict resolution framework is to weaken the preferences of each agent by forgetting certain knowledge that causes inconsistency. In particular, we show how to use the notion of forgetting to provide an elegant solution for preference elicitation in disjunctive logic programming.     

Proving the Asymmetry Thesis Principles for a BDI Agent-Oriented Programming Language

In this paper, we consider each of the nine principles of BDI logics as defined by Rao and Georgeff based on Bratman's asymmetry thesis, and we verify which ones are satisfied by Rao's AgentSpeak(L), a computable logic language inspired by the BDI architecture for cognitive agents. This is in line with Rao's original motivation for defining AgentSpeak(L): to bridge the gap between the theory and practice of BDI agent systems. In order to set the grounds for the proof, we first introduce a particular way in which to define the informational, motivational, and deliberative modalities of BDI logics for AgentSpeak(L) agents, according to its structural operational semantics (that we introduced in a recent paper). This provides a framework that can be used to investigate further properties of AgentSpeak(L) agents, contributing towards giving firm theoretical grounds for BDI agent programming.     

Reasoning about agent deliberation

We present a family of sound and complete logics for reasoning about deliberation strategies for SimpleAPL programs. SimpleAPL is a fragment of the agent programming language 3APL designed for the implementation of cognitive agents with beliefs, goals and plans. The logics are variants of PDL, and allow us to prove safety and liveness properties of SimpleAPL agent programs under different deliberation strategies. We show how to axiomatise different deliberation strategies for SimpleAPL programs, and, for each strategy we prove a correspondence between the operational semantics of SimpleAPL and the models of the corresponding logic. We illustrate the utility of our approach with an example in which we show how to verify correctness properties for a simple agent program under different deliberation strategies.     

A fully abstract model for the exchange of information in multi-agent systems

In this paper,¹ we present a semantic theory for the exchange of information in multi-agent systems. We consider the multi-agent programming language agent communication programming language, which integrates the paradigms of concurrent constraint programming and communicating sequential processes ( ). The constraint programming techniques are used to represent and process information, whereas the synchronous communication mechanism from is generalised to enable the exchange of information. The semantics of the language, which is based on a generalisation of traditional failure semantics, is shown to be fully abstract with respect to observing of each terminating computation its final global store of information.     

A Logic Based Asynchronous Multi-Agent System

We present a logic programming based asynchronous multi-agent system in which agents can communicate with one another; update themselves and each other; abduce hypotheses to explain observations, and use them to generate actions. The knowledge base of the agents is comprised of generalized logic programs, integrity constraints, active rules, and of abducibles. We characterize the interaction among agents via an asynchronous transition rule system, and provide a stable models based semantics. An example is developed to illustrate how our approach works.     

Propositional semantics for disjunctive logic programs

In this paper we study the properties of the class of head-cycle-free extended disjunctive logic programs (HEDLPs), which includes, as a special case, all nondisjunctive extended logic programs. We show that any propositional HEDLP can be mapped in polynomial time into a propositional theory such that each model of the latter corresponds to an answer set, as defined by stable model semantics, of the former. Using this mapping, we show that many queries over HEDLPs can be determined by solving propositional satisfiability problems. Our mapping has several important implications: It establishes the NP-completeness of this class of disjunctive logic programs; it allows existing algorithms and tractable subsets for the satisfiability problem to be used in logic programming; it facilitates evaluation of the expressive power of disjunctive logic programs; and it leads to the discovery of useful similarities between stable model semantics and Clark's predicate completion.     

Autoepistemic logic of first order and its expressive power

We study the expressive power of first-order autoepistemic logic. We argue that full introspection of rational agents should be carried out by minimizing positive introspection and maximizing negative introspection. Based on full introspection, we propose the maximal well-founded semantics that characterizes autoepistemic reasoning processes of rational agents, and show that breadth of the semantics covers all theories in autoepistemic logic of first order, Moore's AE logic, and Reiter's default logic. Our study demonstrates that the autoepistemic logic of first order is a very powerful framework for nonmonotonic reasoning, logic programming, deductive databases, and knowledge representation.This research is partially supported by NSERC grant OGP42193.     

Logic programs with exceptions

We extend logic programming to deal with default reasoning by allowing the explicit representation of exceptions in addition to general rules. To formalise this extension, we modify the answer set semantics of Gelfond and Lifschitz, which allows both classical negation and negation as failure. We also propose a transformation which eliminates exceptions by using negation by failure. The transformed program can be implemented by standard logic programming methods, such as SLDNF. The explicit representation of rules and exceptions has the virtue of greater naturalness of expression. The transformed program, however, is easier to implement.     

Hybrid knowledge bases

Deductive databases that interact with, and are accessed by, reasoning agents in the real world (such as logic controllers in automated manufacturing, weapons guidance systems, aircraft landing systems, land-vehicle maneuvering systems, and air-traffic control systems) must have the ability to deal with multiple modes of reasoning. Specifically, the types of reasoning we are concerned with include, among others, reasoning about time, reasoning about quantitative relationships that may be expressed in the form of differential equations or optimization problems, and reasoning about numeric modes of uncertainty about the domain which the database seeks to describe. Such databases may need to handle diverse forms of data structures, and frequently they may require use of the assumption-based nonmonotonic representation of knowledge. A hybrid knowledge base is a theoretical framework capturing all the above modes of reasoning. The theory tightly unifies the constraint logic programming scheme of Jaffar and Lassez (1987), the generalized annotated logic programming theory of Kifer and Subrahmanian (1989), and the stable model semantics of Gelfond and Lifschitz (1988). New techniques are introduced which extend both the work on annotated logic programming and the stable model semantics     

Causality for debugging mobile agents

Mobile agents, i.e. pieces of programs that can be sent around networks of computers, appear more and more frequently on the Internet. These programs may be seen as an enrichment of traditional distributed computing, and structuring applications using mobile agents is destined to become the de facto way of constructing distributed systems in the near future. Since mobile agents may carry communication links with them as they move across the network, they create very dynamic interconnectio n structures that can be extremely complex to analyse. In this paper we study an example of a system based on the mobile agent principle, written in the Facile programming language. We propose a Structural Operational Semantics (SOS) for Facile, giving a proved transition system that records encodings of the derivation trees of transitions in their labels. This information enables us to easily recover non-interleaving semantics for Facile by looking only at the labels of transitions. We use the new semantics to debug an agent based system. This example is a scaled down version of a system demonstrated at the European IT Conference Exhibition in Brussels, 1995. We also adopt our causal semantics to analyse the specification of a prefetch pipeline processor. Received: 14 November 1997 / 30 April 1999     

A new framework for declarative programming

We propose a new framework for the syntax and semantics of Weak Hereditarily Harrop logic programming with constraints, based on resolution over τ-categories: finite product categories with canonical structure.

Constraint information is directly built-in to the notion of signature via categorical syntax. Many-sorted equational are a special case of the formalism which combines features of uniform logic programming languages (moduels and hypothetical implication) with those of constraint logic programming. Using the cannoical structure supplied by τ-categories, we define a diagrammatic generalization of formulas, goals, programs and resolution proofs up to equality (rather than just up to isomorphism).

We extend the Kowalski-van Emden fixed point interpretation, a cornerstone of declarative semantics, to an operational, non-ground, categorical semantics based on indexing over sorts and programs.

We also introduce a topos-theoretic declarative semantics and show soundness and completeness of resolution proofs and of a sequent calculus over the categorical signature. We conclude with a discussion of semantic perspectives on uniform logic programming.     

Stochastic game logic

Stochastic game logic (SGL) is a new temporal logic for multi-agent systems modeled by turn-based multi-player games with discrete transition probabilities. It combines features of alternating-time temporal logic (ATL), probabilistic computation tree logic and extended temporal logic. SGL contains an ATL-like modality to specify the individual cooperation and reaction facilities of agents in the multi-player game to enforce a certain winning objective. While the standard ATL modality states the existence of a strategy for a certain coalition of agents without restricting the range of strategies for the semantics of inner SGL formulae, we deal with a more general modality. It also requires the existence of a strategy for some coalition, but imposes some kind of strategy binding to inner SGL formulae. This paper presents the syntax and semantics of SGL and discusses its model checking problem for different types of strategies. The model checking problem of SGL turns out to be undecidable when dealing with the full class of history-dependent strategies. We show that the SGL model checking problem for memoryless deterministic strategies as well as the model checking problem of the qualitative fragment of SGL for memoryless randomized strategies is PSPACE-complete, and we establish a close link between natural syntactic fragments of SGL and the polynomial hierarchy. Further, we give a reduction from the SGL model checking problem under memoryless randomized strategies into the Tarski algebra which proves the problem to be in EXPSPACE.     

Goals in conflict: semantic foundations of goals in agent programming

This paper addresses the notion of (declarative) goals as used in agent programming. Goals describe desirable states, and semantics of these goals in an agent programming context can be defined in various ways. We focus in this paper on the representation of conflicting goals. In particular, we define two semantics for goals, one for unconditional goals and one for conditional goals. The first is based on propositional logic, and the latter is based on default logic. We establish relations between and properties of these semantics. This title was inspired by the title of the PhD thesis of Harrenstein: Logic in conflict: logical explorations in strategic equilibrium [25].