在动态认知逻辑中融合观察与内省

一直以来,我们使用逻辑系统来描述数学的证明、结构的计算以及语言的意义。近年来,逻辑系统却越来越多地被用来研究理性行动者的很多方面。例如,如何接受单一的信息,多主体间的交流行为,以及更为一般的受目标驱动的主体间的互动。特别是,对观察和交流中的信息流的研究,大家使用所谓的知识更新、信念修正和偏好改变的动态认知逻辑。当新信息进来时,这些逻辑使用信息的“语义意义”作为被更新状态的选择范围。 然而,同样重要的是,理性主体的行动也会基于其他信息,譬如,由推理和反省得到的信息。对这些问题的研究实际上是对信息在更为语法的意义上进行理解的,即,把信息看作是可以由主体阐明的东西。也许有些奇怪,尽管在这一领域已经存在不少的研究方案,但是大家对什么是信息,信息的关键机制是什么等问题更少有一致的意见。可以看出,对“信息”的意义在逻辑中确实有很多不同的理解。 本文基于可能世界的语义,给出一个一致的信息模型,同时也赋予可能世界语法的“可及通路”。这样,我们就能把外部的“更新信息”和内部的“阐明信息”放在同一个动态逻辑系统中。特别是,我们提出了两个基本的信息行动：纯粹的基于观察的更新（“单纯的看”）和把不明显的知识变为明显知识的“知觉实现”。我们阐明为什么这些行动是自然的,同时我们也提出了一些新的研究问题。其中,很多问题探讨如何使其他的逻辑传统,包括信念修正理论、情景语义学和弗协调逻辑等适用于信息一驱使的理性行动者的图景。     

Conditionals,probability, and non-triviality

We show that the implicational fragment of intuitionism is the weakest logic with a non-trivial probabilistic semantics which satisfies the thesis that the probabilities of conditionals are conditional probabilities. We also show that several logics between intuitionism and classical logic also admit non-trivial probability functions which satisfy that thesis. On the other hand, we also prove that very weak assumptions concerning negation added to the core probability conditions with the restriction that probabilities of conditionals are conditional probabilities are sufficient to trivialize the semantics.     

Logics for Epistemic Programs

We construct logical languages which allow one to represent a variety of possible types of changes affecting the information states of agents in a multi-agent setting. We formalize these changes by defining a notion of epistemic program. The languages are two-sorted sets that contain not only sentences but also actions or programs. This is as in dynamic logic, and indeed our languages are not significantly more complicated than dynamic logics. But the semantics is more complicated. In general, the semantics of an epistemic program is what we call aprogram model. This is a Kripke model of ‘actions’,representing the agents' uncertainty about the current action in a similar way that Kripke models of ‘states’ are commonly used in epistemic logic to represent the agents' uncertainty about the current state of the system. Program models induce changes affecting agents' information, which we represent as changes of the state model, called epistemic updates. Formally, an update consists of two operations: the first is called the update map, and it takes every state model to another state model, called the updated model; the second gives, for each input state model, a transition relation between the states of that model and the states of the updated model. Each variety of epistemic actions, such as public announcements or completely private announcements to groups, gives what we call an action signature, and then each family of action signatures gives a logical language. The construction of these languages is the main topic of this paper. We also mention the systems that capture the valid sentences of our logics. But we defer to a separate paper the completeness proof. The basic operation used in the semantics is called the update product. A version of this was introduced in Baltag et al. (1998), and the presentation here improves on the earlier one. The update product is used to obtain from any program model the corresponding epistemic update, thus allowing us to compute changes of information or belief. This point is of interest independently of our logical languages. We illustrate the update product and our logical languages with many examples throughout the paper.     

Term-Modal Logics

Many powerful logics exist today for reasoning about multi-agent systems, but in most of these it is hard to reason about an infinite or indeterminate number of agents. Also the naming schemes used in the logics often lack expressiveness to name agents in an intuitive way.To obtain a more expressive language for multi-agent reasoning and a better naming scheme for agents, we introduce a family of logics called term-modal logics. A main feature of our logics is the use of modal operators indexed by the terms of the logics. Thus, one can quantify over variables occurring in modal operators. In term-modal logics agents can be represented by terms, and knowledge of agents is expressed with formulas within the scope of modal operators.This gives us a flexible and uniform language for reasoning about the agents themselves and their knowledge. This article gives examples of the expressiveness of the languages and provides sequent-style and tableau-based proof systems for the logics. Furthermore we give proofs of soundness and completeness with respect to the possible world semantics.     

Epistemic closure and commutative, nonassociative residuated structures

K-axiom-based epistemic closure for explicit knowledge is rejected for even the most trivial cases of deductive inferential reasoning on account of the fact that the closure axiom does not extend beyond a raw consequence relation. The recognition that deductive inference concerns interaction as much as it concerns consequence allows for perspectives from logics of multi-agent information flow to be refocused onto mono-agent deductive reasoning. Instead of modeling the information flow between different agents in a communicative or announcement setting, we model the information flow between different states of a single agent as that agent reasons deductively. The resource management of the database of agent states for the deductive reasoning fragment in question is covered by the residuated structure that encodes the nonassociative Lambek Calculus with permutation, bottom, and identity: NLP ₀₁ .     

Probabilistic argumentation

Argumentation in the sense of a process of logical reasoning is a very intuitive and general methodology of establishing conclusions from defeasible premises. The core of any argumentative process is the systematical elaboration, exhibition, and weighting of possible arguments and counter-arguments. This paper presents the formal theory of probabilistic argumentation, which is conceived to deal with uncertain premises for which respective probabilities are known. With respect to possible arguments and counter-arguments of a hypothesis, this leads to probabilistic weights in the first place, and finally to an overall probabilistic judgment of the uncertain proposition in question. The resulting probabilistic measure is called degree of support and possesses the desired properties of non-monotonicity and non-additivity. Reasoning according to the proposed formalism is an simple and natural generalization of the two classical forms of probabilistic and logical reasoning, in which the two traditional questions of the probability and the logical deducibility of a hypothesis are replaced by the more general question of the probability of a hypothesis being logically deducible from the available knowledge base. From this perspective, probabilistic argumentation also contributes to the emerging area of probabilistic logics.     

Towards classifying propositional probabilistic logics

This paper examines two aspects of propositional probabilistic logics: the nesting of probabilistic operators, and the expressivity of probabilistic assessments. We show that nesting can be eliminated when the semantics is based on a single probability measure over valuations; we then introduce a classification for probabilistic assessments, and present novel results on their expressivity. Logics in the literature are categorized using our results on nesting and on probabilistic expressivity.     

What if I am the one? Measuring individual differences in emotional sensitivity to probability and emotional reactivity to possibility

Current theories of risk perception point to the powerful role of emotion and the neglect of probabilistic information in the face of risk, but these tendencies differ across individuals. We propose a method for measuring individuals' emotional sensitivity to probability to assess how feelings about probabilities, rather than the probabilities themselves, influence decisions. Participants gave affective ratings (worry or excitement) to 14 risky events, each with a specified probability ranging from 1 in 10 to 1 in 10,000,000. For each participant, we regressed these emotional responses against item probabilities, estimating a slope (the degree to which emotional responses change with probability) and an intercept (the emotional reaction to an event with a fixed probability). These two parameters were treated as individual difference scores and included in models predicting reactions to several health risk scenarios. Both emotional sensitivity to probability (slope) and emotional reactivity to possibility (intercept) significantly predicted responses to these scenarios, above and beyond the predictive power of other well‐established individual difference measures.     

祈使句和逻辑(英文)

本文基于更新框架定义了一种动态的祈使句语义,并在此基础之上讨论了关于祈使句的逻辑。这个语义对于祈使句的一致性问题和Ross悖论提供了一种直接的解决方案。我们的工作在祈使句和祈使力结构之间的对应之上展开。首先,根据处理祈使句相容性的不同方式,我们给出了几种不同的祈使句一致性定义。然后,祈使句的意义被处理为祈使力机构上的依赖相容性的更新函项。最后,祈使句之间的蕴涵关系被归约为祈使力结构之间的某种关系。基于不同的相容性定义,我们给出了几种不同的蕴涵关系。     

A Logic For Inductive Probabilistic Reasoning

Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from “70% of As are Bs” and “a is an A” infer that a is a B with probability 0.7. Direct inference is generalized by Jeffrey’s rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework for inductive probabilistic reasoning is developed: syntactically it consists of an extension of the language of first-order predicate logic that allows to express statements about both statistical and subjective probabilities. Semantics for this representation language are developed that give rise to two distinct entailment relations: a relation ⊨ that models strict, probabilistically valid, inferences, and a relation that models inductive probabilistic inferences. The inductive entailment relation is obtained by implementing cross-entropy minimization in a preferred model semantics. A main objective of our approach is to ensure that for both entailment relations complete proof systems exist. This is achieved by allowing probability distributions in our semantic models that use non-standard probability values. A number of results are presented that show that in several important aspects the resulting logic behaves just like a logic based on real-valued probabilities alone.     

Diagnostic causal reasoning with verbal information

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people’s inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., “X occasionally causes A”). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes’ prior probabilities and the effects’ likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework.     

What do epistemic logic and cognitive science have to do with each other?

Epistemic logic is a multi-faceted theory aimed at targeting notions such as knowledge, belief, information, awareness, memory and other propositional attitudes, by means of logical and semantical tools. These concepts ought to be in the spotlight of cognitive science too, but the two have not yet seriously been explored in cooperation. In this paper, it is shown that a number of possibilities is opened up by attempting to answer the question of what epistemic logic and cognitive science have to do with each other. Among the proposed answers are: (i) new quantified versions of multi-agent epistemic logic capture locutions involving object identification, giving rise to applications in representing knowledge in multi-agent systems and parallel processing. (ii) The framework of game-theoretic semantics for the ensuing logics enjoys increased cognitive plausibility as the true semantics for epistemic notions. (iii) Several recent findings in cognitive neuroscience pertaining to the notions of awareness and explicit versus implicit processing contribute to logical studies. These three connections are explored here from both logical and cognitive perspectives. Reflecting neuroscientific research, new extensions of epistemic logic are defined, increasing formal understanding of unconscious and unaware information processing in the brain, and making the formalism thus amenable to knowledge representation in multi-agent configurations.     

Counterfactuals,Probabilities, and Information: Response to Critics

In earlier work we proposed an account of information grounded in counterfactual conditionals rather than probabilities, and argued that it might serve philosophical needs that more familiar probabilistic alternatives do not. Demir [2008] and Scarantino [2008] criticize the counterfactual approach by contending that its alleged advantages are illusory and that it fails to secure attractive desiderata. In this paper we defend the counterfactual account from these criticisms, and suggest that it remains a useful account of information.     

Integrating physical constraints in statistical inference by 11-month-old infants

Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a statistical inference mechanism. Results from three experiments suggest that, first, infants were able to use domain-specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain-specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets.     

Probabilistic judgment on a coarser scale

We hypothesize that when assessing the likelihood of uncertain events, statistically unsophisticated people utilize a coarse internal scale that only has a limited number of categories. The present paper reports two experiments on probabilistic judgment to test this hypothesis. In Experiment 1, participants estimated event probabilities freely and we found that their responses were highly clustered despite the even and nearly continuous distribution of the target probabilities. In Experiment 2, participants made forced comparisons using two different external response scales (coarser versus finer). We found that their performance did not measure up to the requirement of the finer scale. These findings indicate that besides the systematic biases, a certain portion of human errors in probabilistic judgment may be due to the low resolution of the internal representations.     

Modal Deduction in Second-Order Logic and Set Theory - II

In this paper, we generalize the set-theoretic translation method for poly-modal logic introduced in [11] to extended modal logics. Instead of devising an ad-hoc translation for each logic, we develop a general framework within which a number of extended modal logics can be dealt with. We first extend the basic set-theoretic translation method to weak monadic second-order logic through a suitable change in the underlying set theory that connects up in interesting ways with constructibility; then, we show how to tailor such a translation to work with specific cases of extended modal logics.     

Generalized Change and the Meaning of Rationality Postulates

The standard theory of belief revision was developed to describe how a rational agent should change his beliefs in the presence of new information. Many interesting tools were created, but the concept of rationality was usually assumed to be related to classical logics. In this paper, we explore the fact that the logical tools used can be extended to other sorts of logics, as proved in (Hansson and Wassermann, 2002), to describe models that are closer to the rationality of a real agent. This revised version was published online in August 2006 with corrections to the Cover Date.     

What is learned from experience in a probabilistic environment?

Three experiments explored what is learned from experience in a probabilistic environment. The task was a simulated medical decision‐making task with each patient having one of two test results and one of two diseases. The test result was highly predictive of the disease for all participants. The base rate of the test result was varied between participants to produce different inverse conditional probabilities of the test result given the disease across conditions. Participants trained using feedback to predict a patient's disease from a test result showed the classic confusion of the inverse error, substituting the forward conditional probability for the inverse conditional probability when tested on it. Additional training on the base rate of the test result did little to improve performance. Training on the joint probabilities, however, produced good performance on either conditional probability. The pattern of results demonstrated that experience with the environment is not always sufficient for good performance. That natural sampling leads to good performance was not supported. Further, because participants not trained on joint probabilities did, however, know them but still committed the confusion of the inverse error, the hypothesis that having joint probabilities would facilitate performance was not supported. The pattern of results supported the conclusion that people learn all the necessary information from experience in a probabilistic environment, but depending upon what the experience was, it may interfere with their ability to recall to memory the appropriate sample set necessary for estimating or using the inverse conditional probability. Copyright © 2004 John Wiley & Sons, Ltd.     

A Primer on Rational Consequence Relations,Popper Functions,and Their Ranked Structures

Rational consequence relations and Popper functions provide logics for reasoning under uncertainty, the former purely qualitative, the latter probabilistic. But few researchers seem to be aware of the close connection between these two logics. I’ll show that Popper functions are probabilistic versions of rational consequence relations. I’ll not assume that the reader is familiar with either logic. I present them, and explicate the relationship between them, from the ground up. I’ll also present alternative axiomatizations for each logic, showing them to depend on weaker axioms than usually recognized.