Engineering principles when coupled with a clear set of goals/constraints/behaviors can help guide the development of effective cognitive architectures.

Creating new concepts from data is a hard problem in the development of cognitive architectures, but one that must be solved for the BICA community to declare success.  Two concept generation algorithms are presented here that are appropriate to different levels of concept abstraction: state-space partitioning with decision trees and context-based similarity.

In this paper, we present a cognitive model of frequency-based decision-making applied to the task of landmine detection. The model is implemented in the ACT-R cognitive architecture and is strongly constrained by the cognitive primitives of the architecture. We then generalize the model to another task in the domain of macroeconomic decision-making using the same architecture, pursuing theoretical parsimony. We describe each model's representation requirements, assess their fits to the data, and analyze their performance scaling as a function of task and architectural parameters. Efforts to generalize the landmine detection model to macroeconomic decision making showed that reasonable fits to the macro-economic performance data could be achieved by models based either on procedural knowledge or declarative knowledge. This finding underscores the importance of distinguishing between processing strategies employed to execute tasks. Such detail appears needed to understand the neural foundations of frequency-based decision-making.

This paper claims for a shift towards "the formal sciences" in the cognitive sciences. In order to explain the phenomenon of cognition, including aspects such as learning and intelligence, it is necessary to explore the concepts and methodologies offered by the formal sciences. In particular, category theory is proposed as the most fitting tool for the building of an unified theory of cognition. This paper proposes a radically new view based in category theory is provided. A cognitive model is informally defined as a mapping between two different structures, while a structure is the set of components of a system and their relationships. Put formally in categorical terms, a model is a functor between categories that reflects the structural invariance between them. In the paper, the theory of categories is presented as the best possible framework to deal with complex system modeling -ie: biologically inspired systems that transcend and offer a much more powerful tool kit to deal with the phenomenon of cognition that other purely verbal tools like the psychological categories that Rosch or Harnad refer.

Competence in interacting with the spatial world, the ability to move around an obstacle, or reach for a desired object, is one of the most immediate needs of any agent existing in such a world. For my thesis work, I am extending a largely-symbolic AI system, the Soar cognitive architecture (Laird, 2008), to better handle spatial problems. A key aspect in the design of Soar is a commitment to generality: the goal of the architecture is to be able to solve the same breadth problems humans are able to solve. In addition, Soar is a psychologically-inspired architecture: a second goal is to solve problems in a manner similar to humans. These goals are reflected in the design of the existing architecture, and must be reflected in the design of any extension to it. Systems for spatial reasoning exist, but they are typically defined for limited domains, and in isolation from a comprehensive intelligent system. My approach to the problem derives from work in diagrammatic reasoning and systems exploring mental imagery. The system augments symbolic working memory in Soar with short-term and long-term memories specialized for spatial information. Reasoning is then a process of manipulating both symbolic and lower-level perceptual data.

We discuss metacognitive modelling as an enhancement to cognitive modelling and computing. Metacognitive control mechanisms should enable AI systems to self-reflect, reason about their actions, and to adapt to new situations. In this respect, we propose implementation details of a knowledge taxonomy and an augmented data mining life cycle which supports a live integration of obtained models.

By way of explaining how a brain works logically, human associative memory is modeled with logical and memory neurons, corresponding to standard digital circuits. The resulting cognitive architecture incorporates basic psychological elements such as short term and long term memory. Novel to the architecture are memory searches using cues chosen pseudorandomly from short term memory. Recalls alternated with sensory images, many tens per second, are analyzed subliminally as an ongoing process, to determine a direction of attention in short term memory.

The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePS-based agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as sentences, so that propositions can occur as argu- ments of propositions and other expressions without leaving first-order logic. The SNePS acting subsystem is integrated with the SNePS reasoning subsystem in such a way that: there are acts that affect what an agent believes; there are acts that specify knowledge-contingent acts and lack-of-knowledge acts; there are policies that serve as "daemons," triggering acts when certain propositions are believed or wondered about. The GLAIR agent architecture supports metacognition by specifying a location for the source of self-awareness and of a sense of situatedness in the world. Several SNePS-based agents have taken advantage of these facilities to engage in self-awareness and metacognition.

In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. I illustrate these ideas using a particular architecture -- ICARUS -- by examining its claims about memories, about the representation and organization of knowledge, and about the performance and learning mechanisms that affect memory structures. In closing, I consider ICARUS's relation to other cognitive architectures and discuss some open issues that deserve increased attention.

In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. I illustrate these ideas using a particular architecture -- ICARUS -- by examining its claims about memories, about the representation and organization of knowledge, and about the performance and learning mechanisms that affect memory structures. I also consider the high-level programming language that embodies these commitments, drawing examples from the domain of in-city driving. In closing, I consider ICARUS's relation to other cognitive architectures and discuss some open issues that deserve increased attention.