Evaluation is a critical activity associated with any theory. Yet this has proven to be a n exceptionally challenging activity for theories based on cognitive architectures. For an overlapping set of reasons, evaluation can also be challenging for theories based on generative neural architectures. T h is dual challenge is approached here by leveraging a broad perspective on theory evaluation to yield a wide - ranging, albeit qualitative, comparison of whole - mind - orie n ted cognitive and generative architectures an d the full systems th a t are based on these architectures .

A beginning is made at mapping four neural theories of consciousness onto the Common Model of Cognition. This highlights how the four jointly depend on recurrent local modules plus a cognitive cycle operating on a global working memory with complex states, and reveals how an existing integrative view of consciousness from a neural perspective aligns with the Com-mon Model.

The Common Model of Cognition (CMC) provides an abstract characterization of the structure and processing required by a cognitive architecture for human-like minds. We propose a unified approach to integrating metacognition within the CMC. We propose that metacog-nition involves reasoning over explicit representations of an agent's cognitive capabilities and processes in working memory. Our proposal exploits the existing cognitive capabilities of the CMC, making minimal extensions in the structure and information available within working memory. We provide examples of metacognition within our proposal.

Model and how we arrived at this proposal. The subsequent The Common Model of Cognition (Rosenbloom, Lebiere & two sections provide more details on two new modules that Laird, 2022) - née the Standard Model of the Mind (Laird, are proposed for inclusion into the Common Model - one for Lebiere & Rosenbloom, 2017) - is a developing consensus emotion and one for metacognitive assessment - and how concerning what must be in a cognitive architecture to they interact with the rest of the model.

The term architecture has evolved considerably from its original Greek roots and its application to buildings and computers to its more recent manifestation for minds. This article considers lessons from this history, in terms of a set of relevant distinctions introduced at each of these stages and a definition of architecture that spans all three, and a reconsideration of three key issues from cognitive architectures for architectures in general and cognitive architectures more particularly.

John Laird, the John L. Tishman Professor of Engineering, has been awarded the 2018 Herbert A. Simon Prize for Advances in Cognitive Systems along with his collaborator Prof. Paul Rosenbloom of the University of Southern California. This award recognizes the pair's research on cognitive architectures, especially their Soar project, their applications to knowledge-based systems and models of human cognition, and their contributions to theories of representation, reasoning, problem solving, and learning. The recipients, the awarding committee writes, have been "energetic contributors to AI and cognitive science" for over 30 years. Laird's and Rosenbloom's interdisciplinary and integrative research, both jointly and individually, has addressed many facets of high-level cognition, and their contributions to Soar have helped create one of the industry's most successful tools for developing intelligent systems. Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior.

Simulations, along with other similar applications like virtual worlds and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Cognitive architectures, which are models of the fixed structure underlying intelligent behavior in both natural and artificial systems, provide a conceptually valid common basis, as evidenced by the current efforts towards a standard model of the mind, to generate human-like intelligent behavior for these synthetic characters. Sigma is a cognitive architecture and system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures, probabilistic graphical models, and more recently neural models, under its graphical architecture hypothesis. Sigma leverages an extended form of factor graphs towards a uniform grand unification of not only traditional cognitive capabilities but also key non-cognitive aspects, creating unique opportunities for the construction of new kinds of cognitive models that possess a Theory-of-Mind and that are perceptual, autonomous, interactive, affective, and adaptive. In this paper, we will introduce Sigma along with its diverse capabilities and then use three distinct proof-of-concept Sigma models to highlight combinations of these capabilities: (1) Distributional reinforcement learning models in; (2) A pair of adaptive and interactive agent models that demonstrate rule-based, probabilistic, and social reasoning; and (3) A knowledge-free exploration model in which an agent leverages only architectural appraisal variables, namely attention and curiosity, to locate an item while building up a map in a Unity environment.

But attempting to model an intelligence after either the ephemeral human mind or the exact physical structure of the brain (rather than iterating increasingly capable Roombas) is no small task -- and with no small amount of competing hypotheses and models to boot. In fact, a 2010 survey of the field found more than two dozen such cognitive architectures actively being studied. The current state of AGI research is "a very complex question without a clear answer," Paul S. Rosenbloom, professor of computer science at USC and developer of the Sigma architecture, told Engadget. "There's the field that calls itself AGI which is a fairly recent field that's trying to define itself in contrast to traditional AI." That is, "traditional AI" in this sense is the narrow, single process AI we see around us in our digital assistants and floor-scrubbing maid-bots.

Interactive simulation environments constitute one of today's promising emerging technologies, with applications in areas such as education, manufacturing, entertainment, and training. These environments are also rich domains for building and investigating intelligent automated agents, with requirements for the integration of a variety of agent capabilities but without the costs and demands of low-level perceptual processing or robotic control. Our project is aimed at developing humanlike, intelligent agents that can interact with each other, as well as with humans, in such virtual environments. Our current target is intelligent automated pilots for battlefield-simulation environments. These dynamic, interactive, multiagent environments pose interesting challenges for research on specialized agent capabilities as well as on the integration of these capabilities in the development of "complete" pilot agents. We are addressing these challenges through development of a pilot agent, ...

It is the ultimate scientific question underlying psychology and AI as well as a substantial part of philosophy: What is the nature of the mind? Newell's autobiography (American Psychological Association 1986) puts it thusly: The central line of Newell's research has remained always the quest for understanding the nature of the mind. The detailed analysis of protocols, the development of production systems, pulling together the theory of human problem solving (in the book of the same name, with Herb Simon), the development of the notion of cognitive architecture, the problem-space hypothesis, a theory of how humans acquire cognitive skills, work on artificial intelligence systems for doing demanding intellectual tasks (such as discovering algorithms), the development of a complete architecture for intelligence--these are some of the main stepping stones. They comprise various mixtures of artificial intelligence and cognitive psychology, as chance and opportunity would have it. This central question will occupy Newell for the rest of his research life, no doubt.