The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

Implicit and explicit learning in ACT-R

A useful way to explain the notions of implicit and explicit learning in ACT-R is to define implicit learning as learning by ACT-R's learning mechanisms, and explicit learning as the results of learning goals. This idea complies with the usual notion of implicit learning as unconscious and always active and explicit learning as intentional and conscious. Two models will be discussed to illustrate this point. First a model of a classical implicit memory task, the SUGARFACTORY scenario by Berry & Broadbent (1984) will be discussed, to show how ACT-R can model implicit learning. The second model is of the so-called Fincham task (Anderson & Fincham, 1994), and exhibits both implicit and explicit learning

A Dynamic ACT-R Model of Simple Games

A model of humans playing the simple game of Paper Rock Scissors based on the ACT-R architecture (Anderson, 1993; Anderson & Lebiere, 1998) is presented. This model stores in long-term memory sequences of moves and attempts to anticipate the opponent's moves by retrieving from memory the most active sequence. This results in a tightly linked dynamical system in which each player drives the play of its opponent. The performance of this model as a function of the length of the sequences stored and the amount of noise in the system is investigated, and is compared to the performance of human subjects

Cognitive Constraints on Decision Making under Uncertainty

doi: 10.3389/fpsyg.2011.0030

Social Networks through the Prism of Cognition

Human relations are driven by social events - people interact, exchange information, share knowledge and emotions, or gather news from mass media. These events leave traces in human memory. The initial strength of a trace depends on cognitive factors such as emotions or attention span. Each trace continuously weakens over time unless another related event activity strengthens it. Here, we introduce a novel Cognition-driven Social Network (CogSNet) model that accounts for cognitive aspects of social perception and explicitly represents human memory dynamics. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves quality of modeling of human interactions in social networks

Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind

In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to indicate a level of analysis, and prediction, of the rational behavior of a cognitive articial agent. This analysis concerns the investigation about the availability of the agent knowledge, in order to pursue its own goals, and is based on the so-called Rationality Principle (an assumption according to which "an agent will use the knowledge it has of its environment to achieve its goals" [22, p. 17]. By using the Newell's own words: "To treat a system at the knowledge level is to treat it as having some knowledge, some goals, and believing it will do whatever is within its power to attain its goals, in so far as its knowledge indicates" [22, p. 13]. In the last decades, the importance of the knowledge level has been historically and system- atically downsized by the research area in cognitive architectures (CAs), whose interests have been mainly focused on the analysis and the development of mechanisms and the processes governing human and (articial) cognition. The knowledge level in CAs, however, represents a crucial level of analysis for the development of such articial general systems and therefore deserves greater research attention [17]. In the following, we will discuss areas of broad agree- ment and outline the main problematic aspects that should be faced within a Common Model of Cognition [12]. Such aspects, departing from an analysis at the knowledge level, also clearly impact both lower (e.g. representational) and higher (e.g. social) levels

A Novel Approach to Intrusion Detection Using a Cognitively-Inspired Algorithm

We propose a novel algorithm for white-box intrusion detection using a cognitive model consistent with the principles of instance-based learning theory. Cognitive models inherit both mechanism and limitations from cognitive architectures implementing unified theories of human cognition. The mechanisms endow the models with powerful characteristics of human cognition, including robustness, generalization and adaptivity. Expanding upon previous research in malware identification and personalized deceptive signaling, the present paper presents a cognitive model able to achieve over 70% accuracy identifying anomalous (vs normal) traffic on the UNSW-NB15 dataset with only 8 features and using only one sample from each attack and 9 normal samples. Accuracy linearly increases to over 85% using up to 100x more samples. A cognitively-inspired salience algorithm then shows the relative impact of each feature driving correct vs incorrect classifications. Implications for integrating this algorithm with human operators are discussed