On Learnability of Restricted Classes of Categorial Grammars

In this paper we present learning algorithms for classes of categorial grammars restricted by negative constraints. We modify learning functions of Kanazawa [10] and apply them to these classes of grammars. We also prove the learnability of intersection of the class of minimal grammars with the class of k-valued grammars. Presented by Wojciech Buszkowski     

Artificial organisms as tools for the development of psychological theory: Tolman’s lesson

In the 1930s and 1940s, Edward Tolman developed a psychological theory of spatial orientation in rats and humans. He expressed his theory as an automaton (the "schematic sowbug") or what today we would call an "artificial organism." With the technology of the day, he could not implement his model. Nonetheless, he used it to develop empirical predictions which tested with animals in the laboratory. This way of proceeding was in line with scientific practice dating back to Galileo. The way psychologists use artificial organisms in their work today breaks with this tradition. Modern "artificial organisms" are constructed a posteriori, working from experimental or ethological observations. As a result, researchers can use them to confirm a theoretical model or to simulate its operation. But they make no contribution to the actual building of models. In this paper, we try to return to Tolman's original strategy: implementing his theory of "vicarious trial and error" in a simulated robot, forecasting the robot's behavior and conducting experiments that verify or falsify these predictions.     

Comparing natural and abstract categories: A case study from computer science

In order to address the issue of whether abstract and concrete objects are categorized similarly, this paper looks at how computer scientists categorize the common concepts of their field. Using the methodology of Rosch, Mervis, Gray, Johnson, and Boyes-Braem (1976) and of Rosch and Mervis (1975) to identify the categorizations used by computer scientists, this study found that the categorization phenomena reported for concrete objects are quite general, operating over a wider range than was previously thought.     

A new member of the family? The continuum of being,artificial intelligence,and the image of God

Abstract

Are the scientific and religious definitions of life irreconcilable or do they overlap in significant areas? What is life? Religion seems to imply that there is a qualitative distinction between human beings and the rest of creation; however, there is a strong tradition in Christianity and in Eastern thought that suggests that the natural world also has a relationship with God. Human dominion over other parts of creation exists, but does not obviate this connection, nor give humans a circle unto themselves. The concept of humans being created in the image of God can be used to explain why we might believe humans are in a circle unto themselves, yet we can expand this concept to include artificially intelligent computers, a new potential member of the cognitive family. Our quest for artificial intelligence tells us both what we value in our humanity, and how we might extend that valuation to the rest of creation.     

人工语法学习中习得知识的分离:基于信号检测论和结构知识的视角

习得知识的意识觉知问题一直以来是内隐学习领域的研究热点和难点。以加工分离程序来分解击中率和虚报率、以结构知识中意识和无意识测量为视角,采用2（测验方式：SDTT,SKT）×2（学习程度：30试次,60试次）混合设计,试图去分离人工语法学习中习得的意识知识和无意识知识。结果发现：（1）学习程度影响到习得的无意识知识,但还未影响到习得的意识知识;（2）在分离人工语法学习的习得知识上,SDTT的敏感性高于SKT;（3）SKT夸大了元认知中的意识成分。     

The influence of complexes on implicit learning

A century ago, Jung looked into the unconscious through complexes by using word association tests. Jung wrote, 'modern psychology with its investigation of complexes has opened up a psychic taboo area riddled with hopes and fears', and complexes remain an unexplored taboo area of research. In the present study, we have investigated the influence of complexes on unconscious cognitive processing, in particular on implicit learning. We have found that complexes shown to disturb conscious cognitive processing in fact enhanced the attention of the subjects and their performance on an implicit learning task. These results suggest that complexes are not just abstractions, but have various actual influences on both conscious and unconscious processing.     

Antecedentes y aparición de la Psicología del Procesamiento de Información: un estudio histórico

Resumen

El presente artículo recopila los grandes hitos asociados al surgimiento de la Psicología del Procesamiento de Información (principales autores, obras, instituciones, eventos científicos, etc.) en Gran Bretaña y Estados Unidos, países en donde este enfoque tiene su origen. Esto se hace a través de una perspectiva de análisis histórico que, remontándonos a los años veinte—y tras pasar revista a los acontecimientos más relevantes para la Historia de la Psicología acaecidos durante los años treinta, cuarenta y cincuenta—, nos conduce a 1958, fecha clave para el nacimiento y difusión de lo que hoy conocemos como Psicología del Procesamiento de Información.     

内隐学习与外显学习的相互关系

对内隐与外显学习过程的相互作用进行了探讨。被试为上海高二学生 10 0人 ,实验通过限定状态语法和双条件语法两种材料的会聚操作对 5种实验条件进行了比较。研究结果表明 :(1)内隐学习具有很强的抽象性 ,本研究首次对此给予了高度评价 ;(2 )内隐与外显学习存在相互作用 ,在学习过程中 ,时而互相促进 ,时而互相冲突 ,时而内隐学习贡献大 ,时而外显学习贡献大 ,本研究探明了内隐学习与外显学习的贡献谁大谁小 ,视学习材料的难度而定。实验启示 ,在人类的学习和生活中 ,内隐学习及其与外显学习的相互作用起着不可估量的作用 !     

Does Wittgenstein Actually Undermine the Foundation of Artificial Intelligence?

Wittgenstein is widely viewed as a potential critic of a key philosophical assumption of the Strong Artificial Intelligence (AI) thesis,namely,that it is in principle possible to build a programmed machine which can achieve real intelligence.Stuart Shanker has provided the most systematic reconstruction of the Wittgensteinian argument against AI,building on Wittgenstein's own statements,the \"rule-following\" feature of language-games,and the putative alliance between AI and psychologism.This article will attempt to refute this reconstruction and its constituent arguments,thereby paving the way for a new and amicable rather than agonistic conception of the Wittgensteinian position on AI.     

Computer supported mathematics with Ωmega

Classical automated theorem proving of today is based on ingenious search techniques to find a proof for a given theorem in very large search spaces—often in the range of several billion clauses. But in spite of many successful attempts to prove even open mathematical problems automatically, their use in everyday mathematical practice is still limited.The shift from search based methods to more abstract planning techniques however opened up a paradigm for mathematical reasoning on a computer and several systems of that kind now employ a mix of interactive, search based as well as proof planning techniques.The Ωmega system is at the core of several related and well-integrated research projects of the Ωmega research group, whose aim is to develop system support for a working mathematician as well as a software engineer when employing formal methods for quality assurance. In particular, Ωmega supports proof development at a human-oriented abstract level of proof granularity. It is a modular system with a central proof data structure and several supplementary subsystems including automated deduction and computer algebra systems. Ωmega has many characteristics in common with systems like NuPrL, CoQ, Hol, Pvs, and Isabelle. However, it differs from these systems with respect to its focus on proof planning and in that respect it is more similar to the proof planning systems Clam and λClam at Edinburgh.