Defeasible Reasoning in Japanese Criminal Jurisprudence

Modeling legal argumentation is one of the most important research in AI and Law, and a lot of models have been proposed. However, most research has not treated value judgement and debate. In this paper, we introduce a legal reasoning model which covers various aspects of legalreasoning such as making argument, selecting argument and debate.Furthermore, we present how criminal law is described and reasoned inthis model.     

TOWARD A COMPUTATIONAL ANALYSIS OF PROBABILISTIC ARGUMENTATION FRAMEWORKS

In this article we analyze probabilistic argumentation frameworks (PAFs), defined as an extension of Dung abstract argumentation frameworks in which each argument n is asserted with a probability p_n. The debate around PAFs has so far centered on their theoretical definition and basic properties. This work contributes to their computational analysis by proposing a first recursive algorithm to compute the probability of acceptance of each argument under grounded and preferred semantics and by studying the behavior of PAFs with respect to reinstatement, cycles, and changes in argument structure. The computational tools proposed may provide strategic information for agents selecting the next step in an open argumentation process and they represent a contribution in the debate about gradualism in abstract argumentation.     

基于辩论理论的多Agent对话系统的研究

多Agent对话系统是一个能够模仿该领域专家进行辩论的计算机程序。它可以根据当前用户输入的信息进行逻辑分析和判断,最终通过辩论得出对该领域某一具体问题的建议。其基本过程是参与对话的Agent根据系统的知识库围绕主题构造出争议,通过对话构造争议之间的攻击关系,最后根据辩论算法确定可接受争议集,该系统能够模拟专家进行分析和决策,以解决那些需要专家才能做出决策的复杂问题。     

Representing Epistemic Uncertainty by Means of Dialectical Argumentation

We articulate a dialectical argumentation framework for qualitative representation of epistemic uncertainty in scientific domains. The framework is grounded in specific philosophies of science and theories of rational mutual discourse. We study the formal properties of our framework and provide it with a game theoretic semantics. With this semantics, we examine the relationship between the snaphots of the debate in the framework and the long run position of the debate, and prove a result directly analogous to the standard (Neyman–Pearson) approach to statistical hypothesis testing. We believe this formalism for representating uncertainty has value in domains with only limited knowledge, where experimental evidence is ambiguous or conflicting, or where agreement between different stakeholders on the quantification of uncertainty is difficult to achieve. All three of these conditions are found in assessments of carcinogenic risk for new chemicals.     

Towards explaining the cognitive efficacy of Euler diagrams in syllogistic reasoning: A relational perspective

Although diagrams have been widely used as methods for introducing students to elementary logical reasoning, it is still open to debate in cognitive psychology whether logic diagrams can aid untrained people to successfully conduct deductive reasoning. In our previous work, some empirical evidence was provided for the effectiveness of Euler diagrams in the process of solving categorical syllogisms. In this paper, we discuss the question of why Euler diagrams have such inferential efficacy in the light of a logical and proof-theoretical analysis of categorical syllogisms and diagrammatic reasoning. As a step towards an explanatory theory of reasoning with Euler diagrams, we argue that the effectiveness of Euler diagrams in supporting syllogistic reasoning derives from the fact that they are effective ways of representing and reasoning about relational structures that are implicit in categorical sentences. A special attention is paid to how Euler diagrams can facilitate the task of checking the invalidity of an inference, a task that is known to be particularly difficult for untrained reasoners. The distinctive features of our conception of diagrammatic reasoning are made clear by comparing it with the model-theoretic conception of ordinary reasoning developed in the mental model theory.     

A logical approach to representing and reasoning about space

The need for a formal language in which to express and reason about spatial concepts is of crucial importance in many areas of AI and visual systems. For the last five years, spatial reasoning research by the Qualitative Spatial Reasoning Group, University of Leeds, has centred on the development and application of such a language — the RCC spatial logic. Below, we briefly describe the work of the group in this area.     

Dominance hierarchies and the evolution of human reasoning

Research from ethology and evolutionary biology indicates the following about the evolution of reasoning capacity. First, solving problems of social competition and cooperation have direct impact on survival rates and reproductive success. Second, the social structure that evolved from this pressure is the dominance hierarchy. Third, primates that live in large groups with complex dominance hierarchies also show greater neocortical development, and concomitantly greater cognitive capacity. These facts suggest that the necessity of reasoning effectively about dominance hierarchies left an indelible mark on primate reasoning architectures, including that of humans. In order to survive in a dominance hierarchy, an individual must be capable of (a) making rank discriminations, (b) recognizing what is forbidden and what is permitted based one's rank, and (c) deciding whether to engage in or refriin from activities that will allow one to move up in rank. The first problem is closely tied to the capacity for transitive reasoning, while the second and third are intimately related to the capacity for deontic reasoning. I argue that the human capacity for these types of reasoning have evolutionary roots that reach deeper into our ancestral past than the emergence of the hominid line, and the operation of these evolutionarily primitive reasoning systems can be seen in the development of human reasoning and domain-specific effects in adult reasoning.     

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.     

定性空间推理的分层递阶框架

定性空间推理是定性推理和空间推理的重要组成部分 .拓扑和形状是定性空间推理研究的关键问题 .针对定性空间推理已有一般框架存在的问题 ,提出了定性空间推理的分层递阶框架 ,并结合拓扑和形状方面的定性空间推理研究工作阐述了所提出的框架的有效性和合理性 .最后总结了分层递阶框架的要点并提出了基于该框架的进一步研究工作 .     

Did he jump or was he pushed?

In this paper, we present a particular role for abductive reasoning in law by applying it in the context of an argumentation scheme for practical reasoning. We present a particular scheme, based on an established scheme for practical reasoning, that can be used to reason abductively about how an agent might have acted to reach a particular scenario, and the motivations for doing so. Plausibility here depends on a satisfactory explanation of why this particular agent followed these motivations in the particular situation. The scheme is given a formal grounding in terms of action-based alternating transition systems and we illustrate the approach with a running legal example.

Many Birds Fly, Some Don't

Ever since the publication of Programs With Common Sense by McCarthy, the problem of qualification has been a source of intense research and debate. While it is undoubtful that now the common sense research community knows a lot about default reasoning, non-monotonic logics, belief revision, multiple extensions, among related topics, it is undeniable that the problem of qualification remains unsolved.In the present paper, the problem of qualification is reframed and a different approach to it is presented. It is here suggested that a more powerful instrument for quantification (instead of the universal quantifier) can circumvent some of the problems raised by the traditional approaches. From a commonsensical point of view, sentences like men are mortal, elephants are grey, and birds fly suggest a kind of partial set inclusion to which decreasing grades of epistemic entrenchment of a certain agent can be associated.In order to illustrate the capabilities of the proposed approach, an exercise of belief revision, involving a mythical australian bird, named Tweety, which happens to be a flying ostrich, is presented.     

THE PRODUCTIVITY PARADOX REVISITED PARODOX REVISITED

The debate about the productivity gains companies have achieved from the use of information technology continues, this time with researchers focusing on the difficulties of productivity measurement. However, there are many other causes of the productivity paradox that have yet to be seriously addressed.     

Process and Policy: Resource-Bounded NonDemonstrative Reasoning

This paper investigates the appropriateness of formal dialectics as a basis for nonmonotonic reasoning and defeasible reasoning that takes computational limits seriously. Rules that can come into conflict should be regarded as policies, which are inputs to deliberative processes. Dialectical protocols are appropriate for such deliberations when resources are bounded and search is serial.
AI, it is claimed here, is now perfectly positioned to correct many misconceptions about reasoning that have resulted from mathematical logic's enormous success in this century: among them, (1) that all reasons are demonstrative, (2) that rational belief is constrained, not constructed, and (3) that process and disputation are not essential to reasoning. AI mainly provides new impetus to formalize the alternative (but older) conception of reasoning, and AI provides mechanisms with which to create compelling formalism that describes the control of processes.
The technical contributions here are: the partial justification of dialectic based on controlling search; the observation that nonmonotonic reasoning can be subsumed under certain kinds of dialectics; the portrayal of inference in knowledge bases as policy reasoning; the review of logics of dialogue and proposed extensions; and the preformal and initial formal discussion of aspects and variations of dialectical systems with nondemonstrative reasons.     

On integration of interface design methods: Can debates be resolved?

There have been many debates on how to design the human–computer interface (HCI). Often, one can find that different views in a debate are simply because these views are attached to different aspects which embody the same thing. In other words, prior to giving an effective judgment of a debate, one needs to establish an understanding of the ‘total’ aspects of a thing the debate is about. Following this line of thinking, in this paper, we propose an understanding of the ‘total’ aspects of designing HCI, which is called the total interface design framework. We then judge several debates under this framework with the purpose of exemplifying the judgment process for any other debate related to designing HCI. At the end, the debates used for exemplifying our judgment process can be resolved. The effectiveness of the total interface design framework for integrating the different HCI approaches is also demonstrated.     

Lessons from Electrification for Identification

As we've seen with the electrical system's evolution and the reengineering of the US voting system, change makes us rediscover essential assumptions and expectations about systems as well as their complexity and inherent risks. In the case of e-voting, the debate reeducated society at large about our expectations of the process, why we have such things as secret ballots, and what it takes to provide confidence that the process has been conducted fairly. With this issue of S&P, we hope that you'll be better prepared to participate in the debate over identity management and ensure that the nontechnical policy makers who will make many of the key decisions can make high-quality choices.     

An embedding of Timed Transition Systems in HOL

The theory of Timed Transition Systems developed by Henzinger, Manna, and Pnueli provides a formal framework for specifying and reasoning about real-time systems. In this paper, we report on some preliminary investigations into the mechanization of this theory using the HOL theorem prover.We review the main ideas of the theory and describe how it has been formally embedded in HOL. A graphical notation of timed transition diagrams and a real-time temporal logic for requirements have also been embedded in HOL using the embedding of timed transition systems. The proof rules proposed by Henzinger et al have been verified formally and we illustrate their use, as well as some problems we have encountered, by reference to a small example. More work is required on interfaces and proof methods to have a generally usable system.     

Qualitative reasoning with directional relations

Qualitative spatial reasoning (QSR) pursues a symbolic approach to reasoning about a spatial domain. Qualitative calculi are defined to capture domain properties in relation operations, granting a relation algebraic approach to reasoning. QSR has two primary goals: providing a symbolic model for human common-sense level of reasoning and providing efficient means for reasoning. In this paper, we dismantle the hope for efficient reasoning about directional information in infinite spatial domains by showing that it is inherently hard to decide consistency of a set of constraints that represents positions in the plane by specifying directions from reference objects. We assume that these reference objects are not fixed but only constrained through directional relations themselves. Known QSR reasoning methods fail to handle this information.     

Context-specific sign-propagation in qualitative probabilistic networks

Qualitative probabilistic networks are qualitative abstractions of probabilistic networks, summarising probabilistic influences by qualitative signs. As qualitative networks model influences at the level of variables, knowledge about probabilistic influences that hold only for specific values cannot be expressed. The results computed from a qualitative network, as a consequence, can be weaker than strictly necessary and may in fact be rather uninformative. We extend the basic formalism of qualitative probabilistic networks by providing for the inclusion of context-specific information about influences and show that exploiting this information upon reasoning has the ability to forestall unnecessarily weak results.     

The epistemic basis of defeasible reasoning

This article argues that: (i) Defeasible reasoning is the use of distinctive procedures for belief revision when new evidence or new authoritative judgment is interpolated into a system of beliefs about an application domain. (ii) These procedures can be explicated and implemented using standard higher-order logic combined with epistemic assumptions about the system of beliefs. The procedures mentioned in (i) depend on the explication in (ii), which is largely described in terms of a Prolog program, EVID, which implements a system for interactive, defeasible reasoning when combined with an application knowledge base. It is shown that defeasible reasoning depends on a meta-level Closed World Assumption applied to the relationship between supporting evidence and a defeasible conclusion based on this evidence. Thesis (i) is then further defended by showing that the EVID explication of defeasible reasoning has sufficient representational power to cover a wide variety of practical applications of defeasible reasoning, especially in the context of decision making.     

Reasoning with Sets of Defaults in Default Logic

We present a general approach for representing and reasoning with sets of defaults in default logic, focusing on reasoning about preferences among sets of defaults. First, we consider how to control the application of a set of defaults so that either all apply (if possible) or none do (if not). From this, an approach to dealing with preferences among sets of default rules is developed. We begin with an ordered default theory , consisting of a standard default theory, but with possible preferences on sets of rules. This theory is transformed into a second, standard default theory wherein the preferences are respected. The approach differs from other work, in that we obtain standard default theories and do not rely on prioritized versions of default logic. In practical terms this means we can immediately use existing default logic theorem provers for an implementation. Also, we directly generate just those extensions containing the most preferred applied rules; in contrast, most previous approaches generate all extensions, then select the most preferred. In a major application of the approach, we show how semimonotonic default theories can be encoded so that reasoning can be carried out at the object level. With this, we can reason about default extensions from within the framework of a standard default logic. Hence one can encode notions such as skeptical and credulous conclusions, and can reason about such conclusions within a single extension.