19,453 research outputs found
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
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
The challenge of complexity for cognitive systems
Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research
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
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
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