The anthropological and economic history of humanity gives evidence of a progression of cognitive frameworks. There are three cognitive perspectives, in order: living in the present, living in the past, and living in the future. They correspond to three levels of competency with abstract thought: concrete thought only, abstract thought with correlations, and abstract thought with both correlations and causality. This appears to explain the fundamental differences between primitive cultures, traditional cultures, and modern cultures: differences in economics, politics, personality, and anthropological differences in general. So, not only does this theory succinctly explain a wide range of human behavior, but because it does, it appears to be a valid theory and a promising way to decompose abstract thought into its component parts for future cognitive research. These frameworks are discussed along with their implications of exploiting this progression to simplify the problem of developing an AI.

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.

Memory retrieval is a spontaneous process difficult to measure in naturalistic settings. By adapting an automated paging process, we measured spontaneous autobiographical and prospective memory retrieval probability, and found the derived frequency of recall in a given time period to be significantly higher than expected. Altogether, this research provides a quantitative characterization of human memory.

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.

For most organisms, there are dangerous objects where even a close encounter with the object could be detrimental. A visual system that helps avoid close approaches with such objects enhances survival probability beyond what is afforded by one that just facilitates simple collision avoidance. However, “dangerous object” is a category that only has meaning in a particular context, and therefore recognizing them is a very complex task. Our objective is to determine how people find dangerous objects in a continuous stream of imagery, and develop a computational implementation of the model that can be tested on imagery. Evidence is cited suggesting that the visual pathways in higher animals implement a Composable Codebook that carries out object recognition. An internal, view-independent world model stores several different types of relational information that make it possible to fill-in incomplete objects and activities once the imagery is registered to the internal world model. Interneurons play a key role in all of the filling-in processes. We illustrate how the models of the visual pathway used by Stephen Grossberg and his group to separate textures can also mediate the registration of an internal world model to visual input.

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.

This paper presents a system for advanced verbal interactions between humans and artificial agents with the aim to learn a simple language in which words and their meaning are grounded in sensory-motor experiences of the agent, and which allows agents to interact and cooperate with humans in shared environments. The system learns grounded language models from examples with a minimum of user intervention and without feedback, and it has been used to understand and subsequently to generate appropriate natural language descriptions of real objects and to engage in verbal interactions with a human partner.

In the past years we developed several design processes (Chella et Perception, also including memory, is one of the most important al. 2006)(Cossentino and Seidita 2004)(Cossentino, Gaglio, features a robotic system must present. In (Chella and Seidita) following the approach based on Situational and Manzotti 2007) it is argued that a perception process can Method Engineering paradigm we fixed in these years be modelled and implemented as a continuous interaction (Cossentino et al. 2007)(Seidita et al. 2009). In the following loop among brain, body and environment; by continuously subsections an overview on the used SME approach, comparing actual and expected "data" coming from the environment the PASSI design process, and the robot perception loop will the robot achieves the ability to gain perceptual be given.

How useful are bio-developmental approaches for understanding how cognitive capabilities are acquired? One bio-developmental hypothesis is that human cognition unfolds with maturation as a massive collection of adaptive cognitive “capabilities” expressing pre-structured genetic programs. But the seeming plasticity of human cognition argues against simple formulations of innately-specified anatomical & functional processing system composed of specialized computational modules. One alternative is an architecture using domain-specific predispositions and general learning mechanisms to construct modules from interactions. This lets them emerge and unfold in a self- organized fashion as part of developmental experience. The result is a more dynamic, complex cognitive architecture explaining such things as the drive for sensorimotor control in infants, which is combines the generation of exploratory movements constrained by the interaction of ability and environment followed by the selection and maintenance of adaptive movement patterns (Schlesinger et al. 2000). Such findings are consistent with a view that ontogenetic processes are co-important (and co-dependent) with gene- based evolutionary processes for behavior and cognition.