First Order Common Knowledge Logics

In this paper we investigate first order common knowledge logics; i.e., modal epistemic logics based on first order logic with common knowledge operators. It is shown that even rather weak fragments of first order common knowledge logics are not recursively axiomatizable. This applies, for example, to fragments which allow to reason about names only; that is to say, fragments the first order part of which is based on constant symbols and the equality symbol only. Then formal properties of "quantifying into" epistemic contexts are investigated. The results are illustrated by means of epistemic representations of Nash Equilibria for finite games with mixed strategies.     

Technical intelligence in animals: the kea model

The ability to act on information flexibly is one of the cornerstones of intelligent behavior. As particularly informative example, tool-oriented behavior has been investigated to determine to which extent nonhuman animals understand means-end relations, object affordances, and have specific motor skills. Even planning with foresight, goal-directed problem solving and immediate causal inference have been a focus of research. However, these cognitive abilities may not be restricted to tool-using animals but may be found also in animals that show high levels of curiosity, object exploration and manipulation, and extractive foraging behavior. The kea, a New Zealand parrot, is a particularly good example. We here review findings from laboratory experiments and field observations of keas revealing surprising cognitive capacities in the physical domain. In an experiment with captive keas, the success rate of individuals that were allowed to observe a trained conspecific was significantly higher than that of naive control subjects due to their acquisition of some functional understanding of the task through observation. In a further experiment using the string-pulling task, a well-probed test for means-end comprehension, we found the keas finding an immediate solution that could not be improved upon in nine further trials. We interpreted their performance as insightful in the sense of being sensitive of the relevant functional properties of the task and thereby producing a new adaptive response without trial-and-error learning. Together, these findings contribute to the ongoing debate on the distribution of higher cognitive skills in the animal kingdom by showing high levels of sensorimotor intelligence in animals that do not use tools. In conclusion, we suggest that the 'Technical intelligence hypothesis' (Byrne, Machiavellian intelligence II: extensions and evaluations, pp 289-211, 1997), which has been proposed to explain the origin of the ape/monkey grade-shift in intelligence by a selection pressure upon an increased efficiency in foraging behavior, should be extended, that is, applied to some birds as well.     

Distant Causation in <Emphasis Type="Italic">C</Emphasis>+

The action language C+ of Giunchiglia, Lee, Lifschitz, McCain and Turner is a high-level, logical formalism for the representation of domains involving action and change. However, one cannot directly express relationships which hold between states more than one time-step distant, or even say that one action determines another at the next time. We present C+_timed, a generalization of C+ which removes these limitations. As for C+, translations to the language of causal theories are given. We also define a new kind of transition system called a ‘run system’ to provide a graphical semantics. Finally, we show how domains involving prohibition and permission can be modelled, by incorporating the ideas of another extension of C+.     

AI &; Law,Logic and Argument Schemes

This paper reviews the history of AI & Law research from the perspective of argument schemes. It starts with the observation that logic, although very well applicable to legal reasoning when there is uncertainty, vagueness and disagreement, is too abstract to give a fully satisfactory classification of legal argument types. It therefore needs to be supplemented with an argument-scheme approach, which classifies arguments not according to their logical form but according to their content, in particular, according to the roles that the various elements of an argument can play. This approach is then applied to legal reasoning, to identify some of the main legal argument schemes. It is also argued that much AI & Law research in fact employs the argument-scheme approach, although it usually is not presented as such. Finally, it is argued that the argument-scheme approach and the way it has been employed in AI & Law respects some of the main lessons to be learnt from Toulmin’s The Uses of Argument.     

Begging the Question in Arguments Based on Testimony

Summary This paper studies some classic cases of the fallacy of begging the question based on appeals to testimony containing circular reasoning. For example, suppose agents a, b and c vouch for d’s credentials, and agents b, d, and e vouch for a’s credentials. Such a sequence of reasoning is circular because a is offering testimony for d but d is offering testimony for a. The paper formulates and evaluates restrictions on the use of testimonial evidence that might be used to deal with such problematic arguments. One is called the Non-repeater Rule: in an extended sequence of argumentation based on testimony, once a source x has been appealed to at any given point in the sequence, that same source x must never be appealed to again at any next point in the same sequence.     

Distinguishing Between Causes and Enabling Conditions—Through Mental Models or Linguistic Cues?

The mental model theory of naive causal understanding and reasoning (Goldvarg & Johnson-Laird, 2001, Cognitive Science, 25, 565–610) claims that people distinguish between causes and enabling conditions on the basis of sets of models that represent possible causal situations. In the tasks used to test this hypothesis, however, the proposed set of models was confounded with linguistic cues that frame which event to assume as given (the enabling condition) and which to consider as responsible for the effect under this assumption (the cause). By disentangling these two factors, we were able to show that when identifying causes and enabling conditions in these tasks, people rely strongly on the linguistic cues but not on the proposed set of models and that this set of models does not even reflect people's typical interpretation of the tasks. We propose an alternative explanation that integrates syntactic and causal considerations.     

大学生亲社会倾向、亲社会推理以及它们的相关模式

采用《亲社会倾向测量》和《亲社会推理客观测量》研究了17至20岁大学生的六类亲社会倾向和亲社会推理的状况。发现大学生将利他的亲社会倾向列为六类倾向中的首位,接下来依次是紧急的、情绪性的、依从的、匿名的和公开的亲社会倾向;在亲社会推理五种类型中大学生选择比率最高的是内化价值定向的推理,接下来依次是刻板定向、需要定向和享乐主义定向的推理,所占比率最低的是赞扬定向的推理;六类亲社会倾向与亲社会推理之间具有特定的棚关模式．利他的和匿名的亲社会倾向与内化价值定向和刻板定向的推理正相关、与享乐主义定向及赞扬定向的推理负相关,公开的亲社会倾向与赞扬定向的推理正相关、与需要定向的推理负相关。跨文化比较证实,中美大学生的上述三个方面表现出一定的差异。     

Acquiring new spatial intuitions: learning to reason about rotations

There are certain simple rotations of objects that most people cannot reason about accurately. Reliable gaps in the understanding of a fundamental physical domain raise the question of how learning to reason in that domain might proceed. Using virtual reality techniques, this project investigated the nature of learning to reason across the domain of simple rotations. Learning consisted of the acquisition of spatial intuitions: there was encoding of useful spatiotemporal information in specific problem types and a gradual accumulation of this understanding across the domain. This pattern of learning through the accumulation of intuitions is especially interesting for rotational motion, in which an elegant domain-wide kinematics is available to support insightful learning. Individual ability to reason about rotations correlated highly with mastery motivation, skill in fluid reasoning, and skill in reasoning about spatial transformations. Thus, general cognitive advantages aided the understanding of individual rotations without guaranteeing immediate generalization across the domain.     

Developing knowledge of objects' motion properties in infancy

Three experiments with a novel variation of the inductive generalization procedure examined 18- and 22-month-olds' knowledge of objects' motion properties. Infants observed simple air and land movements modeled with an appropriate category member (e.g. dog) or an ambiguous block and were allowed to imitate with one or more of four exemplars. The experiments show that 18-month-olds' knowledge of land motions is grounded in causally relevant object parts, whereas 22-month-olds relate such motions more broadly to appropriate category members. Infants' basis for generalizing air motions suggested that at 22 months they have little knowledge about objects from that domain. The results are discussed in relation to the early development of the animate-inanimate distinction and the nature of the inductive generalization task.