Memory systems within a cognitive architecture

A Survey of Cognitive Architectures in the Past 20 Years

Building autonomous systems that achieve human level intelligence is one of the primary objectives in artificial intelligence (AI). It requires the study of a wide range of functions robustly across different phases of human cognition. This paper presents a review of agent cognitive architectures in the past 20 year's AI research. Different from software structures and simulation environments, most of the architectures concerned are established from mathematics and philosophy. They are categorized according to their knowledge processing patterns-symbolic, emergent or hybrid. All the relevant literature can be accessed publicly, particularly through the Internet. Available websites are also summarized for further reference.

Two Models of Moral Judgment

This paper compares two theories and their two corresponding computational models of human moral judgment. In order to better address psychological realism and generality of theories of moral judgment, more detailed and more psychologically nuanced models are needed. In particular, a motivationally based theory of moral judgment (and its corresponding computational model) is developed in this paper that provides a more accurate account of human moral judgment than an existing emotion-reason conflict theory. Simulations based on the theory capture and explain a range of relevant human data. They account not only for the original data that were used to support the emotion-reason conflict theory, but also for a wider range of data and phenomena.

Remembering a Conversation – A Conversational Memory Architecture for Embodied Conversational Agents

This paper addresses the role of conversational memory in Embodied Conversational Agents (ECAs). It describes an investigation into developing such a memory architecture and integrating it into an ECA. ECAs are virtual agents whose purpose is to engage in conversations with human users, typically through natural language speech. While several works in the literature seek to produce viable ECA dialog architectures, only a few authors have addressed the episodic memory architectures in conversational agents and their role in enhancing their intelligence. In this work, we propose, implement, and test a unified episodic memory architecture for ECAs. We describe a process that determines the prevalent contexts in the conversations obtained from the interactions. The process presented demonstrates the use of multiple techniques to extract and store relevant snippets from long conversations, most of whose contents are unremarkable and need not be remembered. The mechanisms used to store, retrieve, and recall episodes from previous conversations are presented and discussed. Finally, we test our episodic memory architecture to assess its effectiveness. The results indicate moderate success in some aspects of the memory-enhanced ECAs, as well as some work still to be done in other aspects.

Cognitive foundations of organizational learning: re-introducing the distinction between declarative and non-declarative knowledge

Contemporary research into socio-cognitive foundations of organizational learning tends to disregard the distinction between declarative and non-declarative knowledge. By reviewing the literature from organizational learning research and cognitive psychology we explain that this distinction is crucial. We describe the foundations of organizational learning by referring to models that consider the interplay between individual and collective knowledge-related processes in organizations. We highlight the existence of a research gap resulting from the finding that these approaches have widely neglected the existence of different types of knowledge. We then elaborate on characteristics of declarative and non-declarative knowledge in general, consider organizations as structures of distributed cognition, and discuss the relationship between organizational knowledge and practice. Subsequently, we examine the role of declarative and non-declarative knowledge in the context of organizational learning. Here, we analyze (1) the cognitive and social mechanisms underlying the development of declarative and non-declarative knowledge within structures of distributed cognition; and (2) the relationship between alterations in declarative and non-declarative types of knowledge on the one hand and changes in organizational practice on the other. Concluding, we discuss implications of our analysis for organizational learning research. We explain how our integrative perspective may offer starting points for a refined understanding of the sub-processes involved in organizational learning and unlearning and may support a better understanding of practical problems related to organizational learning and change.

Explicit versus implicit instruction: which is preferable for learning an artificial morphological rule in children?

INTRODUCTION

It is not clear which method is preferable in the instruction of grammar in children: explicitly, whereby information concerning the nature of the rule to be learned is provided, or implicitly, whereby this information is not provided. In a recent study wherein an artificial morphophonological rule (AMR) was learned without explicit explanation (implicit instruction), unlike 12-year-olds and young adults, 8-year-olds failed to explicitly discover the nature of the rule and to generalize it to novel items. Given that implicit learning mechanisms in children are more mature than explicit ones, here we investigated whether providing explicit explanation can help 8-year-olds learn the AMR.

METHOD

The AMR consisted of phonological transformations of verbs expressing a semantic distinction: whether the preceding noun was animate or inanimate.

RESULTS

The explicit explanation exerted an initial positive effect on learning to apply the AMR to repeated items. Importantly, most explicitly instructed learners were able to generalize the AMR to new items correctly, however, with a small decrease in speed.

DISCUSSION

Despite immature explicit learning mechanisms, explicit explanation followed by practice can trigger the development of explicit knowledge of a complex language rule in children. This method of instruction enabled rule generalization and enhanced processes of proceduralization.