In earlier work, we have shown how a cognitive architecture can be augmented with a diagrammatic reasoning system to produce a bimodal cognitive architecture. In this paper, we show how this bimodal architecture is also bi-representational (multi-representational in the general case) by describing a desiderata for representational formalisms and showing how the diagrammatic representation in biSoar satisfies these requirements.

The goal of this paper is to present four key assumptions regarding the measurement of cognitive and metacognitive regulatory processes used during learning with hypermedia. First, we assume it is possible to detect, trace, model, and foster SRL processes during learning with hypermedia. Second, understanding the complex nature of the regulatory processes during learning with hypermedia is critical in determining why certain processes are used throughout a learning task. Third, it is assumed that the use of SRL processes can dynamically change over time and that they are cyclical in nature (influenced by internal and external conditions and feedback mechanisms). Fourth, capturing, identifying, and classifying SRL processes used during learning with hypermedia is a rather challenging task.

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.

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.

Face recognition under variable lighting conditions is recognized as one of the most problematic are of the recognition domain by various authors. Previous work suggested that image variations caused by parameters such as illumination, can be modeled by low dimensional subspaces. In this work, we propose a new scheme for recognition under a single variation. Using a generic manifold learning technique like LPP, we are able to construct coordinate systems for the underlying subspace with the help of an optimization step. We performed experiments with face recognition under changing illumination conditions.

In this work we will present an integration of a query-answering argumentation approach with an abstract agent programming language. Agents will argumentatively reason via queries, using information of their mental components. Special context-based queries will be used to model the interaction between mental components. Deliberation and execution semantics of the proposed integration are presented.

In this paper a new completely unsupervised mesh segmentation algorithm is proposed, which is based on the PCA interpretation of the Laplacian eigenvectors of the mesh and on parametric clustering using Gaussian mixtures. We analyse the geometric properties of these vectors and we devise a practical method that combines single-vector analysis with multiple-vector analysis. We attempt to characterize the projection of the graph onto each one of its eigenvectors based on PCA properties of the eigenvectors. We devise an unsupervised probabilistic method, based on one-dimensional Gaussian mixture modeling with model selection, to reveal the structure of each eigenvector. Based on this structure, we select a subset of eigenvectors among the set of the smallest non-null eigenvectors and we embed the mesh into the isometric space spanned by this selection of eigenvectors. The final clustering is performed via unsupervised classification based on learning a multi-dimensional Gaussian mixture model of the embedded graph.

A form of categorical perception occurs constantly outside the laboratory, as when different The history of research on speech perception is speakers produce the "same" word or when a speaker says replete with examples of nonlinearities, or threshold the "same" word quickly or slowly. This means that phenomena, relating acoustics to perception. These speech perception cannot be a simple concatenation of nonlinearities are essential in that they allow stable sound elements to yield syllables, syllables to yield communication despite variation in the acoustic signal words, or words to yield sentences. The interdependency across speakers, emphasis, background noise, etc. across scales reveals a complex system with nonlinearly Furthermore, the range of acoustic signals perceived as interacting elements that somehow allow veridical equivalent is much larger for speech sounds than for communication.

This paper describes some functionalities and features of the Maryland Virtual Patient (MVP) environment. MVP models the process of disease progression, diagnosis and treatment in virtual patients who are endowed with a “body,” a simulation of their physiological and pathological processes, and a “mind,” a set of capabilities of perception, reasoning and action that allow the virtual patient to exhibit independent behavior, participate in a natural language dialog, remember events, hold beliefs about other agents and about specific object and event instances, make decisions and learn.

The coagulation system (CS) is a complex, inter-connected biological system with major physiological and pathological roles. The CS may be viewed as a complex adaptive system, in which individual components are linked through multiple feedback and feedforward loops. The non-linear relationships between the numerous coagulation factors and the interplay among the elements of the CS render the study of this biology at a molecular and cellular level nearly impossible. We present an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. Our ABMS approach utilizes a subset of 52 rules to define the interactions among 33 enzymes and factors of the CS. These rules simulate the interaction of each “agent”, such as substrates, enzymes, and cofactors, on a two-dimensional grid of ~12,000 cells and ~300,000 agents. Our ABMS method successfully reproduces the initiation, propagation, and termination of thrombin formation due to the activation of the extrinsic pathway. Furthermore, the ABMS is able to demonstrate the emergence of a threshold effect for thrombin generation as a result of the synergistic effect of combining anticoagulant systems.