Dialectical Argumentation with Argumentation Schemes: An Approach to Legal Logic

This paper describes an approach to legal logic based on the formal analysis of argumentation schemes. Argumentation schemes a notion borrowed from the .eld of argumentation theory - are a kind of generalized rules of inference, in the sense that they express that given certain premises a particular conclusion can be drawn. However, argumentation schemes need not concern strict, abstract, necessarily valid patterns of reasoning, but can be defeasible, concrete and contingently valid, i.e., valid in certain contexts or under certain circumstances. A method is presented to analyze argumentation schemes and it is shown how argumentation schemes can be embedded in a formal model of dialectical argumentation.     

Towards a Formal Account of Reasoning about Evidence: Argumentation Schemes and Generalisations

This paper studies the modelling of legal reasoning about evidence within general theories of defeasible reasoning and argumentation. In particular, Wigmore's method for charting evidence and its use by modern legal evidence scholars is studied in order to give a formal underpinning in terms of logics for defeasible argumentation. Two notions turn out to be crucial, viz. argumentation schemes and empirical generalisations.     

Unifying Agent Systems

Whilst there has been an explosion of interest multi-agent systems, there are still many problems that may have a potentially deleterious impact on the progress of the area. These problems have arisen primarily through the lack of a common structure and language for understanding multi-agent systems, and with which to organise and pursue research in this area. In response to this, previous work has been concerned with developing a computational formal framework for agency and autonomy which, we argue, provides an environment in which to develop, evaluate, and compare systems and theories of multi-agent systems. In this paper we go some way towards justifying these claims by reviewing the framework and showing what we can achieve within it by developing models of agent dimensions, categorising key inter-agent relationships and by applying it to evaluate existing multi-agent systems in a coherent computational model. We outline the benefits of specifying each of the systems within the framework and consider how it allows us to unify different systems and approaches in general.     

Defeasible logic with dynamic priorities

Defeasible logic is a nonmonotonic reasoning approach based on rules and priorities. Its design supports efficient implementation, and it shows promise to be deployed successfully in applications. So far, only static priorities have been used, provided by an external superiority relation. In this article we show how dynamic priorities can be integrated, where priority information is obtained from the deductive process itself. Dynamic priorities have been studied for other related reasoning systems such as default logic and argumentation. We define a proof theory, study its formal properties, and provide an argumentation semantics. © 2004 Wiley Periodicals, Inc.     

认识逻辑(1):关于知识和信念的逻辑框架

知识和信念是人工智能领域研究中经常涉及到的两个重要概念。本文讨论了知识和信念的涵义与关系，定义了认识逻辑系统ＥＩ，讨论了它的语法和语义，证明了认识逻辑ＥＬ不但是可靠的而且是完备的，认为逻辑ＥＬ不但可以用来描述人类的认识过程，还可以用于对常识推理以及分布式系统的形式化描述。     

辩论的逻辑模型研究综述

辩论的逻辑模型是哲学、逻辑学和人工智能等多个领域的研究课题,在非单调推理、法律推理、决策支持和多Agent交互等领域有广泛应用。文中首先简要阐述辩论及辩论模型的基本概念。然后从对辩论建模和用辩论建模两个方面对目前的研究进行总结,分析现有的有影响的辩论模型特点及其存在的问题。最后,指出今后的研究方向和发展趋势。     

Dialogue games that agents play within a society

Human societies have long used the capability of argumentation and dialogue to overcome and resolve conflicts that may arise within their communities. Today, there is an increasing level of interest in the application of such dialogue games within artificial agent societies. In particular, within the field of multi-agent systems, this theory of argumentation and dialogue games has become instrumental in designing rich interaction protocols and in providing agents with a means to manage and resolve conflicts. However, to date, much of the existing literature focuses on formulating theoretically sound and complete models for multi-agent systems. Nonetheless, in so doing, it has tended to overlook the computational implications of applying such models in agent societies, especially ones with complex social structures. Furthermore, the systemic impact of using argumentation in multi-agent societies and its interplay with other forms of social influences (such as those that emanate from the roles and relationships of a society) within such contexts has also received comparatively little attention. To this end, this paper presents a significant step towards bridging these gaps for one of the most important dialogue game types; namely argumentation-based negotiation (ABN). The contributions are three fold. First, we present a both theoretically grounded and computationally tractable ABN framework that allows agents to argue, negotiate, and resolve conflicts relating to their social influences within a multi-agent society. In particular, the model encapsulates four fundamental elements: (i) a scheme that captures the stereotypical pattern of reasoning about rights and obligations in an agent society, (ii) a mechanism to use this scheme to systematically identify social arguments to use in such contexts, (iii) a language and a protocol to govern the agent interactions, and (iv) a set of decision functions to enable agents to participate in such dialogues. Second, we use this framework to devise a series of concrete algorithms that give agents a set of ABN strategies to argue and resolve conflicts in a multi-agent task allocation scenario. In so doing, we exemplify the versatility of our framework and its ability to facilitate complex argumentation dialogues within artificial agent societies. Finally, we carry out a series of experiments to identify how and when argumentation can be useful for agent societies. In particular, our results show: a clear inverse correlation between the benefit of arguing and the resources available within the context; that when agents operate with imperfect knowledge, an arguing approach allows them to perform more effectively than a non-arguing one; that arguing earlier in an ABN interaction presents a more efficient method than arguing later in the interaction; and that allowing agents to negotiate their social influences presents both an effective and an efficient method that enhances their performance within a society.     

Is there a Burden of Questioning?

In some recent cases in Anglo-American law juries ruled contrary to an expert's testimony even though that testimony was never challenged, contradicted or questioned in the trial. These cases are shown to raise some theoretical questions about formal dialogue systems in computational dialectical systems for legal argumentation of the kind recently surveyed by Bench-Capon (1997) and Hage (2000) in this journal. In such systems, there is a burden of proof, meaning that if the respondent questions an argument, the proponent is obliged to offer some support for it give it up. But what should happen in a formal system of dialogue if the proponent puts forward an argument and the respondent fails to critically question it, and simply moves on to another issue? Is this some kind of fault that should have implications? Should it be taken to imply that, by default, the respondent has conceded the argument? What, if anything, should be the outcome of such a failure to question in a formal dialogue system of argumentation? These questions are considered by examining some legal cases of expert opinion testimony in relation rules for formal dialectical argumentation systems.     

Incorporating rhetorical and plausible reasoning in a system for simulating argumentation

The article introduces argumentation theory, some examples of computational models of argumentation, some application examples, considers the significance and problems of argumentation systems, and outlines the significance and difficulties of the field. Also, the article describes a system which used rhetorical reasoning rules such as fairness, reciprocity, and deterrence which was used to simulate the text of a debate. The text was modelled using modern argumentation theory, and this model was used to build the system. The article discusses the system with regard to several aspects: its ability to deal with meaningful contradiction such as claim X supporting claim Y, yet claim Y attacking claim X; recursive arguments; inconsistency maintenance; expressiveness; encapsulation, the use of definitions as the basis for rules, and making generalisations using taxonomies. The article concludes with a discussion of domain dependence, rule plausibility, and some comparisons with formal logic.     

Semantical investigations into nonmonotonic and probabilistic logics

Different forms of semantics have been proposed for conditionals of the form “Usually, if A then B”, ranging from quantitative probability distributions to qualitative approaches using plausibility orderings, possibility distributions, or conditional objects. Atomic-bound systems, also called big-stepped probabilities, allow qualitative reasoning with probabilities, aiming at bridging the gap between qualitative and quantitative argumentation and providing a model for the nonmonotonic reasoning system P. By using Goguen and Burstall’s notion of institutions for the formalization of logical systems, we elaborate precisely which formal connections exist among big-stepped probabilities, standard probabilities, and qualitative logics. Based on our investigations, we also develop two variants of conditional objects, one of them having a simpler semantics while still providing a model for system P.     

Temporal Development Methods for Agent-Based

In this paper we overview one specific approach to the formal development of multi-agent systems. This approach is based on the use of temporal logics to represent both the behaviour of individual agents, and the macro-level behaviour of multi-agent systems. We describe how formal specification, verification and refinement can all be developed using this temporal basis, and how implementation can be achieved by directly executing these formal representations. We also show how the basic framework can be extended in various ways to handle the representation and implementation of agents capable of more complex deliberation and reasoning.This revised version was published online in August 2005 with a corrected cover date.     

The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma

Many economic and social systems are essentially large multi-agent systems.By means of computational modeling, the complicated behavior of such systemscan be investigated. Modeling a multi-agent system as an evolutionary agentsystem, several important choices have to be made for evolutionary operators.Especially, it is to be expected that evolutionary dynamics substantiallydepend on the selection scheme. We therefore investigate the influence ofevolutionary selection mechanisms on a fundamental problem: the iteratedprisoner's dilemma (IPD), an elegant model for the emergence of cooperationin a multi-agent system. We observe various types of behavior, cooperationlevel, and stability, depending on the selection mechanism and the selectionintensity. Hence, our results are important for (1) the proper choice andapplication of selection schemes when modeling real economic situations and(2) assessing the validity of the conclusions drawn from computer experimentswith these models. We also conclude that the role of selection in theevolution of multi-agent systems should be investigated further, for instanceusing more detailed and complex agent interaction models.     

A Constraint-Based Robotic Soccer Team

It is a challenging task for a team of multiple fast-moving robots to cooperate with each other and to compete with another team in a dynamic, real-time environment. For a robot team to play soccer successfully, various technologies have to be incorporated including robotic architecture, multi-agent collaboration and real-time reasoning. A robot is an integrated system, with a controller embedded in its plant. A robotic system is the coupling of a robot to its environment. Robotic systems are, in general, hybrid dynamic systems, consisting of continuous, discrete and event-driven components. Constraint Nets (CN) provide a semantic model for modeling hybrid dynamic systems. Controllers are embedded constraint solvers that solve constraints in real-time. A controller for our robot soccer team, UBC Dynamo98, has been modeled in CN, and implemented in Java, using the Java Beans architecture. A coach program using an evolutionary algorithm has also been designed and implemented to adjust the weights of the constraints and other parameters in the controller. The results demonstrate that the formal CN approach is a practical tool for designing and implementing controllers for robots in multi-agent real-time environments. They also demonstrate the effectiveness of applying the evolutionary algorithm to the CN-modeled controllers.     

基于描述逻辑的主体服务匹配

多主体系统中的服务匹配是智能主体和多主体系统等领域中的重要研究课题．描述逻辑是知识表示和推理的形式化工具，它提供了可判定的和可靠的推理服务．该文利用描述逻辑有效的推理功能，特别是它对概念包含关系的有效判断，把它与多主体系统的服务推理结合起来．充分利用描述逻辑具有清晰模型一理论语义和有效的概念分层推理服务等功能，该文提出了基于描述逻辑的主体服务匹配算法，详细研究了如何利用描述逻辑的理论和推理机制来实现自动的服务分层及服务匹配．并提出了五种服务匹配算法．这些方法都是基于语义的服务匹配，利用服务分层机制实现了有效和高效的多主体系统中的服务匹配，克服了基于语义距离进行服务匹配的不足．     

Formalizing dialectical explanation support for argument-based reasoning in knowledge-based systems

The concept of explanation has received attention from different areas in Computer Science, particularly in the knowledge-based systems and expert systems communities. At the same time, argumentation has evolved as a new paradigm for conceptualizing commonsense reasoning, resulting in the formalization of different argumentation frameworks and the development of several real-world argument-based applications. Although the notions of explanation and argument for a claim share many common elements in knowledge-based systems their interrelationships have not yet been formally studied in the context of the current argumentation research in Artificial Intelligence. This article explores these ideas by providing a new perspective on how to formalize dialectical explanation support for argument-based reasoning. To do this, we propose a formalization of explanations for abstract argumentation frameworks with dialectical constraints where different emerging properties are studied and analyzed. As a concrete example of the formalism introduced we show how it can be fleshed out in an implemented rule-based argumentation system.     

A taxonomy of argumentation models used for knowledge representation

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.     

Baseballs and arguments from fairness

This paper applies two argumentation schemes, argument from fairness and argument from lack of knowledge (along with other schemes of lesser prominence) to model the reasoning given by Judge McCarthy supporting his decision to divide the proceeds of a homerun baseball in the case of Popov v. Hayashi. Several versions of both schemes are explained and discussed, and then applied to the argumentation given by Judge McCarthy as the basis of the reasoning used to arrive at his decision. The scheme for argument from fairness is shown to be based on a special principle in Perelman’s theory of justice.     

Distributed Computation Multi-agent System

This article addresses a formal model of a distributed computation multi-agent system. This model has evolved from the experimental research on using multi-agent systems as a ground for developing fuzzy cognitive maps. The main paper contribution is a distributed computation multi-agent system definition and mathematical formalization based on automata theory. This mathematical formalization is tested by developing distributed computation multi-agent systems for fuzzy cognitive maps and artificial neural networks – two typical distributed computation systems. Fuzzy cognitive maps are distributed computation systems used for qualitative modeling and behavior simulation, while artificial neural networks are used for modeling and simulating complex systems by creating a non-linear statistical data model. An artificial neural network encapsulates in its structure data patterns that are hidden in the data used to create the network. Both of these systems are well suited for formal model testing. We have used evolutionary incremental development as an agent design method which has shown to be a good approach to develop multi-agent systems according to the formal model of a distributed computation multi-agent system.     

There's Nothing Like a Good Argument ...

Since computing pioneers Vannevar Bush and Doug Engelbart envisioned computational support for argumentation, many have pursued the exciting vision of tools for capturing and augmenting collective reasoning. Designers would be able to capture their deliberations on the fly during design sessions, with intuitive visualizations assisting participatory analysis by diverse stakeholders. These traces would later help recover design rationale. When managing requirements, we can think of argument schemes as reusable patterns for tightening up deliberations. Project reviews are an obvious candidate, where decisions must be justified, often to be signed off, and resources committed. As meeting capture becomes a practical reality, we have the basis for requirements platforms that provide new forms of multimedia requirements and rationale traceability.     

Preferences in artificial intelligence

The paper presents a focused survey about the presence and the use of the concept of “preferences” in Artificial Intelligence. Preferences are a central concept for decision making and have extensively been studied in disciplines such as economy, operational research, decision analysis, psychology and philosophy. However, in the recent years it has also become an important topic both for research and applications in Computer Science and more specifically in Artificial Intelligence, in fields spanning from recommender systems to automatic planning, from non monotonic reasoning to computational social choice and algorithmic decision theory. The survey essentially covers the basics of preference modelling, the use of preference in reasoning and argumentation, the problem of compact representations of preferences, preference learning and the use of non conventional preference models based on extended logical languages. It aims at providing a general reference for all researchers both in Artificial Intelligence and Decision Analysis interested in this exciting interdisciplinary topic.