Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents

We present a logic-based formalism for modeling ofdialogues between intelligent and autonomous software agents,building on a theory of abstract dialogue games which we present.The formalism enables representation of complex dialogues assequences of moves in a combination of dialogue games, and allowsdialogues to be embedded inside one another. The formalism iscomputational and its modular nature enables different types ofdialogues to be represented.     

A Logic for Characterizing Multiple Bounded Agents

We describe a meta-logic for characterizing the evolving internal reasoning of various families of agents. We view the reasoning of agents as ongoing processes rather than as fixed sets of conclusions. Our approach utilizes a strongly sorted calculus, distinguishing the application language, time, and various syntactic sorts. We have established soundness and completeness results corresponding to various families of agents. This allows for useful and intuitively natural characterizations of such agents' reasoning abilities. We discuss and contrast consistency issues as in the work of Montague and Thomason. We also show how to represent the concept of focus of attention in this framework.     

Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning

In this paper we advocate the use of multi-dimensional modal logics as a framework for knowledge representation and, in particular, for representing spatio-temporal information. We construct a two-dimensional logic capable of describing topological relationships that change over time. This logic, called PSTL (Propositional Spatio-Temporal Logic) is the Cartesian product of the well-known temporal logic PTL and the modal logic S4_u, which is the Lewis system S4 augmented with the universal modality. Although it is an open problem whether the full PSTL is decidable, we show that it contains decidable fragments into which various temporal extensions (both point-based and interval based) of the spatial logic RCC-8 can be embedded. We consider known decidability and complexity results that are relevant to computation with multi-dimensional formalisms and discuss possible directions for further research.     

Epistemic Logic for Rule-Based Agents

The logical omniscience problem, whereby standard models of epistemic logic treat an agent as believing all consequences of its beliefs and knowing whatever follows from what else it knows, has received plenty of attention in the literature. But many attempted solutions focus on a fairly narrow specification of the problem: avoiding the closure of belief or knowledge, rather than showing how the proposed logic is of philosophical interest or of use in computer science or artificial intelligence. Sentential epistemic logics, as opposed to traditional possible worlds approaches, do not suffer from the problems of logical omniscience but are often thought to lack interesting epistemic properties. In this paper, I focus on the case of rule-based agents, which play a key role in contemporary AI research but have been neglected in the logical literature. I develop a framework for modelling monotonic, nonmonotonic and introspective rule-based reasoners which have limited cognitive resources and prove that the resulting models have a number of interesting properties. An axiomatization of the resulting logic is given, together with completeness, decidability and complexity results.     

A Default Logic Based Framework for Context-Dependent Reasoning with Lexical Knowledge

Lexical knowledge is increasingly important in information systems—for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Information about contexts can be reasoned with as first-order formulae formulae; and (4) Information about contexts can be derived as default inferences. In the dex framework, a word for which lexical knowledge is sought is called a query word. The context for a query word is derived from further words, such as words in the same sentence as the query word. These further words are used with a form of decision tree called a context classification tree to identify which contexts hold for the query word. We show how we can use these contexts in default logic to identify lexical knowledge about the query word such as synonyms, antonyms, specializations, meronyms, and more sophisticated first-order semantic knowledge. We also show how we can use a standard machine learning algorithm to generate context classification trees.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

A Formal Framework for Interactive Agents

This paper proposes a formal framework and architecture for specification and analysis of interactive agents. The framework can be used to explore the design space, study features of different points in the design space, and to develop executable specifications of specific agents and study their interactions with the environment. A long term goal is development of reasoning principles specialized to different regions of the design space.     

A Simulation Framework for Heterogeneous Agents

We introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling, and flexible specification of design parameters. One key aspect of this framework is the design specification: we use a format based on the Extendible Markup Language (XML) that is simple-structured yet still enables the design of flexible models. Another issue in agent-based simulations, especially when ready-made components are used, is the heterogeneity arising from both the agents' implementations and the underlying platforms. To tackle such obstacles, we introduce a wrapper technique for mapping the functionality of agents living in an interpreter-based environment to a standardized JAVA interface, thus facilitating the task for any control mechanism (like a simulation manager) because it has to handle only one set of commands for all agents involved. Again, this mapping is made by an XML-based definition format. We demonstrate the technique by applying it to a simple sample simulation of two mass marketing firms operating in an artificial consumer environment.     

ExpertClerk: A Conversational Case-Based Reasoning Tool forDeveloping Salesclerk Agents in E-Commerce Webshops

Conversational Case-based Reasoning (CCBR) has been used successfully toimprove knowledge management in corporate activities as a problemsolver. In our past research, we developed CCBR systems in customersupport domains where CCBR systems played the role of customer supportagents. Based on these experiences, we have applied the same CCBRtechnologies to design the user-interface of e-commerce websites.ExpertClerk was designed as a tool for developing dialogue-basedfront-end systems for product databases. We first analyzed conversationmodels of human salesclerks interacting with customers. The goal of asalesclerk is to effectively match a customer's buying points and aproduct's selling points. To achieve this, the salesclerk alternatesbetween asking questions, proposing sample products, and observing thecustomer's responses. ExpertClerk imitates a human salesclerk. Itconsolidates the human shopper's requests by narrowing down a list ofmany products through a process of asking effective questions usingentropy (navigation-by-asking) and showing contrasting samples with anexplanation of their selling points (navigation-by-proposing). Thisrequest elaboration cycle is repeated until the shopper finds anappropriate product. In this article, we present the systemarchitecture, algorithms as well as empirical evaluations.     

A Dialogue Game Protocol for Agent Purchase Negotiations

We propose a dialogue game protocol for purchase negotiation dialogues which identifies appropriate speech acts, defines constraints on their utterances, and specifies the different sub-tasks agents need to perform in order to engage in dialogues according to this protocol. Our formalism combines a dialogue game similar to those in the philosophy of argumentation with a model of rational consumer purchase decision behaviour adopted from marketing theory. In addition to the dialogue game protocol, we present a portfolio of decision mechanisms for the participating agents engaged in the dialogue and use these to provide our formalism with an operational semantics. We show that these decision mechanisms are sufficient to generate automated purchase decision dialogues between autonomous software agents interacting according to our proposed dialogue game protocol.     

DynamiCS: An Actor-Based Framework for Negotiating Mobile Agents

In this article, a framework to integrate negotiation capabilities—particularly, components implementing a negotiation strategy—into mobile agents is described. This approach is conceptually based on the notion of an actor system which decomposes an application component into autonomously executing subcomponents cooperating with each other. Technically, the framework is based on a plug-in mechanism enabling a dynamic composition of negotiating agents. Additionally, this contribution describes how interaction-oriented rule mechanisms can be deployed to control the behavior of strategy actors.     

一种采用模糊逻辑方法的卖方报价策略

随着Internet的快速发展,电子商贸应运而生。由于商贸业务的复杂性,所以在电子商贸应用系统中通常采用具有一定自主特性和智能特性的Agent(软件实体)模拟商贸业务中的活动主体,由此产生了多Agent之间的交易协商技术的研究。该文提出了一种基于模糊逻辑方法的能够适用于电子商贸活动中的卖方Agent的报价策略。此策略运用了模糊规则和模糊推理机制,能很好地适应交易信息的不完整性和不确定性。     

A Branching Time Temporal Framework for Quantitative Reasoning

Temporal logics such as Computation Tree Logic (CTL) and Linear Temporal Logic (LTL) have become popular for specifying temporal properties over a wide variety of planning and verification problems. In this paper we work towards building a generalized framework for automated reasoning based on temporal logics. We present a powerful extension of CTL with first-order quantification over the set of reachable states for reasoning about extremal properties of weighted labeled transition systems in general. The proposed logic, which we call Weighted Quantified Computation Tree Logic (WQCTL), captures the essential elements common to the domain of planning and verification problems and can thereby be used as an effective specification language in both domains. We show that in spite of the rich, expressive power of the logic, we are able to evaluate WQCTL formulas in time polynomial in the size of the state space times the length of the formula. Wepresent experimental results on the WQCTL verifier.     

A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation

This paper describes a negotiation model that incorporates real-time issues for autonomous agents. This model consists of two important ideas: a real-time logical negotiation protocol and a case-based negotiation model. The protocol integrates a real-time Belief-Desire-Intention (BDI) model, a temporal logic model, and communicative acts for negotiation. This protocol explicitly defines the logical and temporal relationships of different knowledge states, facilitating real-time designs such as multi-threaded processing, state profiling and updating, and a set of real-time enabling functional predicates in our implementation. To further support the protocol, we use a case-based reasoning model for negotiation strategy selection. An agent learns from its past experience by deriving a negotiation strategy from the most similar and useful case to its current situation. Guided by the strategy, the agent negotiates with its partners using an argumentation-based negotiation protocol. The model is time and situation aware such that each agent changes its negotiation behavior according to the progress and status of the ongoing negotiation and its current agent profile. We apply the negotiation model to a resource allocation problem and obtain promising results.     

A Hybrid Logic for Reasoning about Knowledge and Topology

We extend Moss and Parikh’s bi-modal system for knowledge and effort by means of hybrid logic. In this way, some additional concepts from topology related to knowledge can be captured. We prove the soundness and completeness as well as the decidability of the extended system. Special emphasis will be placed on algebras.     

Fuzzy Logic: Misconceptions and Clarifications

Some commonly accepted statements concerning the basic fuzzy logicproposed by Lotfi Zadeh in 1965, have led to suggestions that fuzzy logicis not a logic in the same sense as classical bivalent logic. Thoseconsidered herein are: fuzzy logic generates results that contradictclassical logic, fuzzy logic collapses to classical logic, there can be no prooftheory for fuzzy logic, fuzzy logic is inconsistent, fuzzy logic producesresults that no human can accept, fuzzy logic is not proof-theoreticcomplete, fuzzy logic is too complex for practical use, and, finally, fuzzylogic is not needed. It is either proved or argued herein that all of the thesestatements are false and are, hence, misconceptions. A fuzzy logic withtruth values specified as subintervals of the real unit interval [0.0, 1.0] isintroduced. Proofs of the correctness, consistency, and proof theoreticcompleteness of the truth interval fuzzy logic are either summarized orcited. It is concluded that fuzzy logics deserve the accolade of logic tothe same degree that the term applies to classical logics.     

Web-Based Adaptive Tutoring: An Approach Based on Logic Agents and Reasoning about Actions

In this paper we describe an approach to the construction of adaptive tutoring systems, based on techniques from the research area of Reasoning about Actions and Change.This approach leads to the implementation of aprototype system, having a multi-agent architecture,whose kernel is a set of rational agents,programmed in the logic programminglanguage DyLOG. In the prototype that weimplemented the reasoning capabilities of theagents are exploited both to dynamically buildstudy plans and to verify the correctness ofuser-given study plans with respect to thecompetence that the user wants to acquire.     

'Classical' Negation in Nonmonotonic Reasoning and Logic Programming

Gelfond and Lifschitz were the first to point out the need for a symmetric negation in logic programming and they also proposed a specific semantics for such negation for logic programs with the stable semantics, which they called 'classical'. Subsequently, several researchers proposed different, often incompatible, forms of symmetric negation for various semantics of logic programs and deductive databases. To the best of our knowledge, however, no systematic study of symmetric negation in non-monotonic reasoning was ever attempted in the past. In this paper we conduct such a systematic study of symmetric negation. We introduce and discuss two natural, yet different, definitions of symmetric negation: one is called strong negation and the other is called explicit negation. For logic programs with the stable semantics, both symmetric negations coincide with Gelfond–Lifschitz' 'classical negation'. We study properties of strong and explicit negation and their mutual relationship as well as their relationship to default negation 'not', and classical negation '¬'. We show how one can use symmetric negation to provide natural solutions to various knowledge representation problems, such as theory and interpretation update, and belief revision. Rather than to limit our discussion to some narrow class of nonmonotonic theories, such as the class of logic programs with some specific semantics, we conduct our study so that it is applicable to a broad class of non-monotonic formalisms. In order to achieve the desired level of generality, we define the notion of symmetric negation in the knowledge representation framework of AutoEpistemic logic of Beliefs, introduced by Przymusinski.