The identity of every human subject is imprinted in the subject’s mind. This work explores one possible approach to authentication of the user identity using implicit long-term memory, specifically, memory for faces.

This work presents a general-purpose character reasoning model intended for usage by autonomous team actors that are acting as believable characters (e.g., human team actors fall into this category). The idea is that selecting a cast of believable characters can predetermine a solution to an unexpected challenge that the team may be facing in a rescue mission. This approach in certain cases proves more efficient than an alternative approach based on rational decision making and planning, which ignores the question of character believability. This point is illustrated with a simple numerical example in a virtual world paradigm.

A candidate framework for integration of theoretical, modeling and experimental approaches to understanding emotional intelligence is described. The framework includes three elements of a new kind that enable representation of emotional cognition: an emotional state, an appraisal, and a moral schema. These elements are integrated with the weak semantic cognitive map representing the values of emotional appraisals. The framework is tested on interpretation of results obtained in two new experimental paradigms that reveal general features of human emotional cognition, such as the emergence of subjectively perceived persistent roles of individual virtual actors. Implications concern heterogeneous human-robot teams.

Emotional intelligence is the key for acceptance of intelligent agents by humans as equal partners, e.g., in ad hoc teams. At the same time, its existing implementations in intelligent agents are mostly limited to basic affects. Currently, there is no consensus in the understanding of complex and social emotions at the level of functional and computational models. The approach of this work is based on the mental state formalism, originally developed as a part of the cognitive architecture GMU BICA and recently extended to include affective building blocks (A.V. Samsonovich, AAAI Technical Report WS-12-06: 109-116, 2012). In the present work, complex social emotions like humor, jealousy, compassion, shame, pride, etc. are identified as emergent patterns of appraisals represented by schemas, that capture the cognitive nature of these emotions and enable their modeling. A general model of complex emotions and emotional relationships is constructed that can be validated by simulations of emotionally biased interactions and emergent relationships in small groups of agents. The framework will be useful in cognitive architectures for designing human-like-intelligent social agents possessing a sense of humor and other human-like emotionally intelligent capabilities.

In order to understand the role of metacognition and self-regulation in student learning, 35 college students were asked to solve problems in college linear algebra and in remedial math using Cognitive Constructor. Results reveal the predominance of forward chaining in problem solving.

The present historical epoch is unique in the sense that now The present work takes a shot at this target. The author's people may have the opportunity to create something equal answer to the first question should be clear from the above to them, if not greater: machines capable of humanlike and can be formulated concisely as follows: the goal is to intellectual and cultural development. The reason is not design a human-level learner. Yet, this statement needs a only that the hardware available today is compatible in its further clarification. Its limited interpretation could be, raw computational capacities with the human brain. The e.g.: "The goal of a human-level learner is to take complex, main reason is the emergent understanding of how the noisy information from multiple modalities and distill this human mind works. It appears that implementing the same experience into a representation that supports prediction principles of the human mind in a machine would not take about and manipulation of the world" (Shrobe et al., 2006, yet unavailable today computer resources.

The challenge of designing a human-level learner is central to creating a computational equivalent of the human mind. It demands the level of robustness and flexibility of learning that is still only available in biological systems. Therefore, it is essential that we better understand at a computational level how biological systems naturally develop their cognitive and learning functions. In recent years, biologically inspired cognitive architectures (BICA) have emerged as a powerful new approach toward gaining this kind of understanding. The impressive success of BICA-2008 was clear evidence of this trend. As the second event in the series, BICA-2009 continues our attack on the challenge, with the overall atmosphere of excitement and promise, brainstorming, and collaboration.

Such model could also provide a basis for simulation of anatomically realistic virtual neurons [1]. The model should accurately distinguish among different neuronal classes: a morphological difference between classes would be captured by a difference in model parameters and reproduced in generated virtual neurons. In addition, the model should be self-consistent: there should be no statistical difference in model parameters measured from real neurons of a given class and from virtual neurons of the same class. The assumption that a simple statistical model of this sort exists relies on the similarity of average environmental and homeostatic conditions encountered by individual neurons during development and on the limited amount of genetic information that underlies differentiation of neuronal classes. Previous research in computational neuroanatomy has mainly focused on the topology and internal geometry of dendrites (i.e., the properties described in "dendrograms") [2,3].

Such model could also provide a basis for simulation of anatomically realistic virtual neurons [1]. The model should accurately distinguish among different neuronal classes: a morphological difference between classes would be captured by a difference in model parameters and reproduced in generated virtual neurons. In addition, the model should be self-consistent: there should be no statistical difference in model parameters measured from real neurons of a given class and from virtual neurons of the same class. The assumption that a simple statistical model of this sort exists relies on the similarity of average environmental and homeostatic conditions encountered by individual neurons during development and on the limited amount of genetic information that underlies differentiation of neuronal classes. Previous research in computational neuroanatomy has mainly focused on the topology and internal geometry of dendrites (i.e., the properties described in "dendrograms") [2,3].