A true human-level learner should be able to deliberately construct its own knowledge, its processes of reasoning resulting in a new knowledge, its system of values and goals, and the scenario of its cognitive growth. These capabilities require a cognitive architecture of a new kind that supports metacognition, self-awareness and self-regulation. An example architecture design called Constructor is described in this work. The main distinguishing feature of this architecture is its virtually unlimited self-regulated cognitive growth ability. Other features include metacognition, self-awareness, and an intrinsic embodiment in virtual reality that is used, e.g., for active construction of cognitive and learning processes.

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.

Oscillatory neural networks have been an increasing focus of study over the last several years. Here we consider simple oscillatory memories for short-term retention of items occurring as temporal sequences. By incorporating decay as well as interference, we find that it is easy to match behavioral data from human subjects recalling temporal sequences under different situations by adjusting a single parameter in the model. These results suggest that simple oscillatory memories capture at least some key properties of human short-term memory, and might be used effectively in future biologically-inspired cognitive architectures.

The Institute of Education Sciences (IES) provides funding opportunities for researchers to bring their knowledge of learning, cognitive science, and technology to bear on education practice. This panel describes opportunities available through the National Center for Education Research and the National Center for Special Education Research.

A deeply-interactive hybrid neural-symbolic cognitive architecture is defined as one in which the neural-net and symbolic components interact frequently and dynamically, so that each intervenes significantly in the other's internal operations, and the two form a combined dynamical system at the time-scale of each component's individual cognitive operations.  An example architecture of this nature that is currently under development is described: OpenCog NS, based on integration of the OpenCog cognitive architecture (which incorporates symbolic, evolutionary and connectionist aspects) with a hierarchical attractor neural network (HANN).  In this integrated architecture, the neural and non-neural aspects each play major roles, and the depth of the interconnection is revealed for example in the facts that symbolic reasoning intervenes in the process of attractor formation within the HANN, whereas the HANN plays a major role in guiding the individual steps of logical inference and evolutionary program learning processes.

This paper argues that supervised cognitive growth in artifacts will be very difficult to achieve without detailed knowledge about systems’ internal states. Physical information is too low level to provide a useful understanding of a system’s behavior, and it is more pragmatically useful to take a mental stance towards an artificial system and interpret its actions in terms of mental states. This mental stance is similar to Dennett’s intentional stance, except the ascription of beliefs and rationality in the intentional stance is replaced by the attribution of low level mental states in the mental stance. In some cases it might also be useful to take a conscious stance towards an artificial system that interprets its behavior as the outcome of a conscious decision making process. Since most artifacts lack language, automatic analysis techniques have to be used to identify the contents of their minds, and the second half of this paper suggests how some of the earlier work of Aleksander and Atlas can be applied in this area.

Many biological models of human motivation and behavior posit a functional division between those subsystems respon- sible for approach and avoidance behaviors. Gray and McNaughton's (2000) revised Reinforcement Sensitivity Theory (RST) casts this distinction in terms of a Behavioral Activation System (BAS) and a Fight-Flight-Freeze System (FFFS), mediated by a third, conflict resolution system — the Behavioral Inhibition System (BIS). They argued that these are fundamental, functionally distinct systems. The model has been highly influential both in personality psychology, where it provides a biologically-based explanation of traits such as extraversion and neuroticism, and in clinical psychology wherein state disorders such as Major Depressive Disorder and Generalized Anxiety Disorder can be modeled as differences in baseline sensitivities of one or more of the systems. In this paper, we present work in progress on implementing a simplified simulation of RST in a set of embodied virtual characters. We argue that RST provides an interesting and potentially powerful starting point for cognitive architectures for various applications, including interactive entertainment, in which simulation of human-like affect and personality is important.

Decades ago, Alan Turing proposed a test to show if a machine has intelligence, a test that has yet to be replaced by a more comprehensive theory. The same test however, says nothing about what is intelligence. This paper proposes a definition based on a system ability to deal with uncertainty, which is the main attribute of our intelligence. It introduces a new adaptive system theory and the Viable Complex System (VCS), concept that is applied to organisms, social organizations, and to the design and architecture of IT systems. All VCSs share a dual structure built on two function types: operations (i.e. resource processing) and change (adaptability). A system adapts by learning from the interactions with environment on how to improve its chances to survive. All systems sharing common operations are part of a realm. Obviously, we may have systems which could live in two realms at the same time. In conclusion, we define information as the interaction between two similar VCSs, and intelligence as a property of adaptive systems which exist in the context of two realms (i.e. humans being biological organisms and members of the society). We extend the model to quantify intelligence through the use of a new term called information density. This concept associates complexity of the logic embedded in a message, especially the one related to changes, with the system ability to process that logic in its quest to survive. The more intelligent the system, the better it is at extracting information towards higher efficiency and higher viability. We are closing the paper with the presentation of two case studies from our practice that shows how this model can be applied in the IT when designing enterprise systems.

Consciousness is a major feature of mammalian nervous systems. Recent evidence indicates it may extend from mammals to birds and even cephalopods (Edelman, Seth 2009). Since all major biological adaptations are functional, or sequelae of biofunctions, and since brains perform computations, it would seem that consciousness must have a basic biocomputational function. Biologically, that means of course that consciousness endows nervous systems with one or more adaptive advantages leading to higher gene frequencies for those brains. Given that mammals have existed for some 200 million years, and that mammals share the thalamocortical core that supports conscious states, it is very likely that conscious brains have gathered not just one but many biocomputational functions. That certainly accords with our common sense notions of conscious (as well as unconscious) activities.

Many aspects can be taken into account in order to assess the power and potential of a cognitive architecture. In this paper we argue that ConsScale, a cognitive scale inspired on the development of consciousness, can be used to characterize and evaluate cognitive architectures from the point of view of the effective integration of their cognitive functionalities. Additionally, a graphical characterization of the cognitive power of artificial agents is proposed as a helpful tool for the analysis and comparison of Machine Consciousness implementations. This is illustrated with the application of the scale to a particular problem domain in the context of video game synthetic bots.