This article presents a process account of some typicality effects and related similarity-dependent accuracy and response time phenomena that arise in the context of supervised concept acquisition. We describe Symbolic Concept Acquisition (SCA), a computational system that acquires and activates category prediction rules. In contrast to gradient representations, SCA performs by probing for prediction rules in a series of discrete steps. For learning new rules, it acquires general rules but then incrementally learns more specific ones. In describing SCA, we emphasize (...) its functionality in terms of accuracy and efficiency and motivate its design within the set of symbolic mechanisms and memory structures defined by the Soar architecture (Laird, Newell & Rosenbloom, 1987). For replicating human behavior, we first show how SCA exhibits some typicality effects in the course of learning responding faster and more accurately to more typical test examples. Then, using data from human experiments, we evaluate SCA's qualitative predictions on accuracy and response time on individual dataset instances. We show how SCA's predictions correlate with human data across three experimental conditions concerning the effect of instruction on learning strategy. (shrink)

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive (...) scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation. (shrink)

It is now more than ten years since the EU FET project ALFEBIITE finished, during which its researchers made original and distinctive contributions to (inter alia) formal models of trust, model-checking, and action logics. ALFEBIITE was also a highly inter-disciplinary project, with partners from computer science, philosophy, cognitive science and law. In this paper, we reflect on the interaction between computer scientists and information and IT lawyers on the idea of the ‘open agent society’. This inspired a programme of research (...) whose investigation into conceptual challenges has carried it from the logical specification of agent societies and dynamic norm-governed systems to self-organising electronic institutions, while developing several technologies for agent-based modelling and complex event recognition. The outcomes of this inter-disciplinary collaboration have also influenced current research into using the open agent society as a platform for socio-technical systems, and other collective adaptive systems. We present a number of research challenges, including the ideas of computational justice and polycentric governance, and explore a number of ethical, legal and social implications. We contend that, in order to address these issues and challenges, the continued inter-disciplinary collaboration between computer science and IT lawyers is critical. (shrink)

Rules are a common symbolic model of knowledge. Rule-based systems share roots in cognitive science and artificial intelligence. In the former, they are mostly used in cognitive architectures; in the latter, they are developed in several domains including knowledge engineering and machine learning. This paper aims to give an overview of these issues with the focus on the current research perspective of artificial intelligence. Moreover, in this setting we discuss our results in the design of rule-based systems and their applications (...) in context-aware and business intelligence systems. (shrink)