Common-sense reasoning as proto-scientific agent activity

We wish to model common-sense reasoning in situations where it contains some of the ingredients typical of proto-scientific reasoning, with a view to future elaboration and proof of concept. To model this proto-scientific narrative, we employ the integrative formal computational machinery we have been developing and implementing for rational cooperative epistemic agents. In our logic-based framework, agents can update their own and each other's theories, which are comprised of knowledge, active rules, integrity constraints, queries, abducibles, and preferences; they can engage in abductive reasoning involving updatable preferences; set each other queries; react to circumstances; plan and carry out actions; and revise their theories and preferences by means of concurrent updates on self and others.     

Methodological naturalism and epistemic internalism

Epistemic naturalism holds that the results or methodologies from the cognitive sciences are relevant to epistemology, and some have maintained that scientific methods are more compatible with externalist theories of justification than with internalist theories. But practically all discussions about naturalized epistemology are framed exclusively in terms of cognitive psychology, which is only one of the cognitive sciences. The question addressed in this essay is whether a commitment to naturalism really does favor externalism over internalism, and we offer reasons for thinking that naturalism in epistemology is compatible with both internalist and externalist conceptions of justification. We also argue that there are some distinctively internalist aims that are currently being studied scientifically and these notions, and others, should be studied by scientific methods. This essay is dedicated to Deborah Mayo, who has long advocated using error statistical techniques to analyze and resolve epistemological puzzles in the philosophy of science. This essay follows the same spirit by advocating that computational concepts and techniques be applied within the heart of traditional, analytic epistemology.     

A logical expression of reasoning

A non-monotonic logic, the Logic of Plausible Reasoning (LPR), capable of coping with the demands of what we call complex reasoning, is introduced. It is argued that creative complex reasoning is the way of reasoning required in many instances of scientific thought, professional practice and common life decision taking. For managing the simultaneous consideration of multiple scenarios inherent in these activities, two new modalities, weak and strong plausibility, are introduced as part of the Logic of Plausible Deduction (LPD), a deductive logic specially designed to serve as the monotonic support for LPR. Axiomatics and semantics for LPD, together with a completeness proof, are provided. Once LPD has been given, LPR may be defined via a concept of extension over LPD. Although the construction of LPR extensions is first presented in standard style, for the sake of comparison with existing non-monotonic formalisms, alternative more elegant and intuitive ways for constructing non-monotonic LPR extensions are also given and proofs of their equivalence are presented.     

An encompassing framework for Paraconsistent Logic Programs

We propose a framework which extends Antitonic Logic Programs [Damásio and Pereira, in: Proc. 6th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Springer, 2001, p. 748] to an arbitrary complete bilattice of truth-values, where belief and doubt are explicitly represented. Inspired by Ginsberg and Fitting's bilattice approaches, this framework allows a precise definition of important operators found in logic programming, such as explicit and default negation. In particular, it leads to a natural semantical integration of explicit and default negation through the Coherence Principle [Pereira and Alferes, in: European Conference on Artificial Intelligence, 1992, p. 102], according to which explicit negation entails default negation. We then define Coherent Answer Sets, and the Paraconsistent Well-founded Model semantics, generalizing many paraconsistent semantics for logic programs. In particular, Paraconsistent Well-Founded Semantics with eXplicit negation (WFSX_p) [Alferes et al., J. Automated Reas. 14 (1) (1995) 93–147; Damásio, PhD thesis, 1996]. The framework is an extension of Antitonic Logic Programs for most cases, and is general enough to capture Probabilistic Deductive Databases, Possibilistic Logic Programming, Hybrid Probabilistic Logic Programs, and Fuzzy Logic Programming. Thus, we have a powerful mathematical formalism for dealing simultaneously with default, paraconsistency, and uncertainty reasoning. Results are provided about how our semantical framework deals with inconsistent information and with its propagation by the rules of the program.     

Consistency Defaults

A consistency default is a propositional inference rule that asserts the consistency of a formula in its consequence. Consistency defaults allow for a straightforward encoding of domains in which it is explicitely known when something is possible. The logic of consistency defaults can be seen as a variant of cumulative default logic or as a generalization of justified default logic; it is also able to simulate Reiter default logic in the seminormal case. A semantical characterization of consistency defaults in terms of processes and in terms of a fixpoint equation is given, as well as a normal form. Presented by Melvin Fitting     

Propositional Plausible Logic: Introduction and Implementation

Plausible Logic allows defeasible deduction with arbitrary propositions, and yet when sufficiently simplified it is very similar to the Defeasible Logics of Billington and Nute. This paper presents Plausible Logic, explains some of the ideas behind the definitions, applies Plausible Logic to an example, and proves a coherence result which indicates that Plausible Logic is well behaved. We also report the first complete implementation of propositional Plausible Logic. The implementation has a web interface which makes it available to researchers and students everywhere. The implementation is evaluated experimentally, and is shown to be capable of handling tens of thousands of rules and sufficiently many disjunctions for realistic problems.     

A formalisation of violation, error recovery, and enforcement in the bit transmission problem

The design of complex multi-agent systems is increasingly having to confront the possibility that agents may not behave as they are supposed to. In addition to analysing the properties that hold if protocols are followed correctly, it is also necessary to predict, test, and verify the properties that would hold if these protocols were to be violated. We illustrate how the formal machinery of deontic interpreted systems can be applied to the analysis of such problems by considering three variations of the bit transmission problem. The first, an example in which an agent may fail to do something it is supposed to do, shows how we deal with violations of protocols and specifications generally. The second, an example in which an agent may do something it is not supposed to do, shows how it is possible to specify and analyse remedial or error-recovery procedures. The third combines both kinds of faults and introduces a new component into the system, a controller, whose role is to enforce compliance with the protocol. In each case the formal analysis is used to test whether critical properties of the system are compromised, in this example, the reliable communication of information from one agent to the other.     

Monotonicity and Reasoning with Exceptions

A proposal by Ferguson [2003, Argumentation 17, 335–346] for a fully monotonic argument form allowing for the expression of defeasible generalizations is critically examined and rejected as a general solution. It is argued that (i) his proposal reaches less than the default-logician’s solution allows, e.g., the monotonously derived conclusion is one-sided and itself not defeasible. (ii) when applied to a suitable example, his proposal derives the wrong conclusion. Unsuccessful remedies are discussed.     

An epistemic and dynamic approach to abductive reasoning: Abductive problem and abductive solution

We propose a study of abductive reasoning addressing it as an epistemic process that involves both an agent?s information and the actions that modify this information. More precisely, we present and discuss definitions of an abductive problem and an abductive solution in terms of an agent?s information, that is, in terms of knowledge and beliefs. The discussion is then formalised by ‘implementing’ our definitions in a dynamic epistemic logic framework, where the properties of these definitions are studied, an epistemic action that represents the application of an abductive step is introduced, and an illustrative example is provided. A number of the most interesting properties of abductive reasoning (those highlighted by Peirce) are shown to be better modelled within this approach.     

Termination of logic programs with imperfect information: applications and query procedure

A general logic programming framework allowing for the combination of several adjoint lattices of truth-values is presented. The language is sorted, enabling the combination of several reasoning forms in the same knowledge base. The contribution of the paper is two-fold: on the one hand, sufficient conditions guaranteeing termination of all queries for the fix-point semantics for a wide class of sorted multi-adjoint logic programs are presented and related to some well-known probability-based formalisms; in addition, we specify a general non-deterministic tabulation goal-oriented query procedure for sorted multi-adjoint logic programs over complete lattices. We prove its soundness and completeness as well as independence of the selection ordering. We apply the termination results to probabilistic and fuzzy logic programming languages, enabling the use of the tabulation proof procedure for query answering.     

Executable specifications for hypothesis-based reasoning with Prolog and Constraint Handling Rules

Constraint Handling Rules (CHR) is an extension to Prolog which opens up a spectrum of hypothesis-based reasoning in logic programs without additional interpretation overhead. Abduction with integrity constraints is one example of hypothesis-based reasoning which can be implemented directly in Prolog and CHR with a straightforward use of available and efficiently implemented facilities.The present paper clarifies the semantic foundations for this way of doing abduction in CHR and Prolog as well as other examples of hypothesis-based reasoning that is possible, including assumptive logic programming, hypotheses with priority and scope, and nonmonotonic reasoning.Examples are presented as executable code so the paper may also serve the additional purpose of a practical guide for developing such programs, and it is demonstrated that the approach provides a seamless integration with existing constraint solvers.     

Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

In this paper, it is argued that single function dual process theory is a more credible psychological account of non-monotonicity in human conditional reasoning than recent attempts to apply logic programming (LP) approaches in artificial intelligence to these data. LP is introduced and among other critiques, it is argued that it is psychologically unrealistic in a similar way to hash coding in the classicism vs. connectionism debate. Second, it is argued that causal Bayes nets provide a framework for modelling probabilistic conditional inference in System 2 that can deal with patterns of inference LP cannot. Third, we offer some speculations on how the cognitive system may avoid problems for System 1 identified by Fodor in 1983. We conclude that while many problems remain, the probabilistic single function dual processing theory is to be preferred over LP as an account of the non-monotonicity of human reasoning.     

Logic programming,probability, and two-system accounts of reasoning: a rejoinder to Oaksford and Chater (2014)

This reply to Oaksford and Chater’s (O&C)’s critical discussion of our use of logic programming (LP) to model and predict patterns of conditional reasoning will frame the dispute in terms of the semantics of the conditional. We begin by outlining some common features of LP and probabilistic conditionals in knowledge-rich reasoning over long-term memory knowledge bases. For both, context determines causal strength; there are inferences from the absence of certain evidence; and both have analogues of the Ramsey test. Some current work shows how a combination of counting defeaters and statistics from network monitoring can provide the information for graded responses from LP reasoning. With this much introduction, we then respond to O&C’s specific criticisms and misunderstandings.     

一种应用于中医阴阳五行诊断学说的次协调逻辑(英文)

中医阴阳五行学说博大情深,是中医辨证论治的理论基础,也是指导中医临床诊断与治疗的基本原理,因此对于中医的理论与实践都有着十分重要的价值。但长期以来,一直缺乏对该学说进行严格的形式化研究,使之难以与不断发展中的中医现代化进程相适应。本文就是在这一学说核心内容的基础上,介绍引入次协调逻辑、缺省推理逻辑等先进逻辑来与中医诊断学结合的思想和方法,这样的目的是为今后构造一种符合中医诊断原则的中医阴阳五行推演逻辑系统作准备,该逻辑系统将用来描述基本的中医辨证推演过程。     

Inferences by exclusion in the great apes: the effect of age and species

This study investigated the ability of chimpanzees, gorillas, orangutans, and bonobos to make inferences by exclusion using the procedure pioneered by Premack and Premack (Cognition 50:347–362, 1994) with chimpanzees. Thirty apes were presented with two different food items (banana vs. grape) on a platform and covered with identical containers. One of the items was removed from the container and placed between the two containers so that subjects could see it. After discarding this item, subjects could select between the two containers. In Experiment 1, apes preferentially selected the container that held the item that the experimenter had not discarded, especially if subjects saw the experimenter remove the item from the container (but without seeing the container empty). Experiment 3 in which the food was removed from one of the containers behind a barrier confirmed these results. In contrast, subjects performed at chance levels when a stimulus (colored plastic chip: Exp. 1; food item: Exp. 2 and Exp. 3) designated the item that had been removed. These results indicated that apes made inferences, not just learned to use a discriminative cue to avoid the empty container. Apes perceived and treated the item discarded by the experimenter as if it were the very one that had been hidden under the container. Results suggested a positive relationship between age and inferential ability independent of memory ability but no species differences.This contribution is part of the special issue “Animal Logics” (Watanabe and Huber 2006).