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134
Cognitive architectures: Research issues and challenges
, 2002
"... In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representat ..."
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Cited by 108 (13 self)
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In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1
Instance-based learning in dynamic decision making
- Cognitive Science
, 2003
"... This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-base ..."
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Cited by 104 (44 self)
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This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision-making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic-based to instance-based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT’s learning mechanisms have been implemented in an ACT-R cognitive model. Through a series of experiments, this paper shows how the IBLT’s learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.
The interaction of the explicit and the implicit in skill learning: A dual process approach.
- Psychological Review,
, 2005
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Tacit knowledge and knowledge conversion: Controversy and advancement in organizational knowledge creation theory.
- Organization science
, 2009
"... N onaka's paper [1994. A dynamic theory of organizational knowledge creation. Organ. Sci. 5(1) 14-37] contributed to the concepts of "tacit knowledge" and "knowledge conversion" in organization science. We present work that shaped the development of organizational knowledg ..."
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Cited by 62 (0 self)
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N onaka's paper [1994. A dynamic theory of organizational knowledge creation. Organ. Sci. 5(1) 14-37] contributed to the concepts of "tacit knowledge" and "knowledge conversion" in organization science. We present work that shaped the development of organizational knowledge creation theory and identify two premises upon which more than 15 years of extensive academic work has been conducted: (1) tacit and explicit knowledge can be conceptually distinguished along a continuum; (2) knowledge conversion explains, theoretically and empirically, the interaction between tacit and explicit knowledge. Recently, scholars have raised several issues regarding the understanding of tacit knowledge as well as the interaction between tacit and explicit knowledge in the theory. The purpose of this article is to introduce and comment on the debate about organizational knowledge creation theory. We aim to help scholars make sense of this debate by synthesizing six fundamental questions on organizational knowledge creation theory. Next, we seek to elaborate and advance the theory by responding to questions and incorporating new research. Finally, we discuss implications of our endeavor for organization science.
Making children gesture brings out implicit knowledge and leads to learning
- Journal of Experimental Psychology: General
, 2007
"... Speakers routinely gesture with their hands when they talk, and those gestures often convey information not found anywhere in their speech. This information is typically not consciously accessible, yet it provides an early sign that the speaker is ready to learn a particular task (S. Goldin-Meadow, ..."
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Cited by 41 (19 self)
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Speakers routinely gesture with their hands when they talk, and those gestures often convey information not found anywhere in their speech. This information is typically not consciously accessible, yet it provides an early sign that the speaker is ready to learn a particular task (S. Goldin-Meadow, 2003). In this sense, the unwitting gestures that speakers produce reveal their implicit knowledge. But what if a learner was forced to gesture? Would those elicited gestures also reveal implicit knowledge and, in so doing, enhance learning? To address these questions, the authors told children to gesture while explaining their solutions to novel math problems and examined the effect of this manipulation on the expression of implicit knowledge in gesture and on learning. The authors found that, when told to gesture, children who were unable to solve the math problems often added new and correct problem-solving strategies, expressed only in gesture, to their repertoires. The authors also found that when these children were given instruction on the math problems later, they were more likely to succeed on the problems than children told not to gesture. Telling children to gesture thus encourages them to convey previously unexpressed, implicit ideas, which, in turn, makes them receptive to instruction that leads to learning.
Theories of Artificial Grammar Learning
, 2007
"... Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Th ..."
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Cited by 34 (3 self)
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Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Theoretical accounts of AGL are reviewed, together with relevant human experimental and neuroscience data. The author concludes that satisfactory understanding of AGL requires (a) an understanding of implicit knowledge as knowledge that is not consciously activated at the time of a cognitive operation; this could be because the corresponding representations are impoverished or they cannot be concurrently supported in working memory with other representations or operations, and (b) adopting a frequency-independent view of rule knowledge and contrasting rule knowledge with specific similarity and associative learning (co-occurrence) knowledge.
Solving the symbol grounding problem: a critical review of fifteen years of research
- Journal of Experimental and Theoretical Artificial Intelligence
, 2005
"... It is a publisher's requirement to display the following notice: The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maint ..."
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Cited by 27 (4 self)
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It is a publisher's requirement to display the following notice: The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder. 1
Cognitive architectures and general intelligent systems
- AI Magazine
, 2006
"... The original goal of artificial intelligence was the design and construction of computational artifacts that combined many cognitive abilities in an integrated system. These entities were intended to have the same intellectual capacity as humans and they were supposed to exhibit their intelligence i ..."
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Cited by 26 (2 self)
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The original goal of artificial intelligence was the design and construction of computational artifacts that combined many cognitive abilities in an integrated system. These entities were intended to have the same intellectual capacity as humans and they were supposed to exhibit their intelligence in a general way across many different domains. We will refer to this research agenda as aimed at the creation of general intelligent systems. Unfortunately, modern artificial intelligence has largely abandoned this objective, having instead divided into many distinct subfields that care little about generality, intelligence, or even systems. Subfields like computational linguistics, planning, and computer vision focus their attention on specific components that underlie intelligent behavior, but seldom show concern about how they might interact with each other. Subfields like knowledge representation and machine learning focus on idealized tasks like inheritance, classification, and reactive control that ignore the richness and complexity of human intelligence. The fragmentation of artificial intelligence has taken energy away from efforts on general intelligent systems, but it has led to certain types of progress within each of its subfields. Despite this subdivision into distinct communities, the past decade has seen many applications of AI technology
Learning Behavior-Selection by Emotions and Cognition in a Multi-Goal Robot Task
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... The existence of emotion and cognition as two interacting systems, both with important roles in decision-making, has been recently advocated by neurophysiological research (LeDoux, 1998, Damasio, 1994). Following that idea, this paper presents the ALEC agent architecture which has both emotive an ..."
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Cited by 26 (0 self)
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The existence of emotion and cognition as two interacting systems, both with important roles in decision-making, has been recently advocated by neurophysiological research (LeDoux, 1998, Damasio, 1994). Following that idea, this paper presents the ALEC agent architecture which has both emotive and cognitive learning, as well as emotive and cognitive decision-making capabilities to adapt to real-world environments. These two learning mechanisms embody very different properties which can be related to those of natural emotion and cognition systems. The reported
Sequence Learning: From Recognition and Prediction to Sequential Decision Making”,
- IEEE Intelligent Systems,
, 2001
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