The brain's ability to do this in a self-stabilizing fashion employs several different types of predictive mechanisms. The lack of a single such mechanism is clarified by accumulating theoretical and empirical evidence that brain specialization is governed by computationally complementary processing streams. The present talk will discuss recent progress towards explaining fundamental brain processes such as 3D vision in natural scenes; opticflow based navigation in natural scenes towards goals around obstacles and spatial navigation in the dark; object and scene learning, recognition, and search; cognitiveemotional dynamics that direct motivated attention towards valued goals; adaptive sensory-motor control circuits, such as those that coordinate predictive smooth pursuit and saccadic eye movements; and planning circuits that temporarily represent sequences of events in working memory and learn sequential plans, including repeated events or actions. These competences clarify the global system-level organization as well as the local microcircuit level organization of many brain systems, ranging from form and motion streams in the visual cortex through inferotemporal and parietal cortex, perirhinal and parahippocampal cortex; supplementary and frontal eye fields; orbitofrontal, ventrolateral, and dorsolateral prefrontal cortex; entorhinal and hippocampal cortex; and subcortical areas including basal ganglia, amygdala, superior colliculus, and nucleus reticularis tegmenti pontis. These model systems are being transferred as they become ready to a wide variety of large-scale applications in technology.

In this work an AI/ICT Platform is presented, to develop cognitive networks to cope with a management of a great availability of data and a necessity to dispose of prompt right information, extracted by data. In fact, the better strategic decision arise by a prompt availability of target and effective information. A cognitive network, and in particular an intelligent grid, helps to reach this goal. This intelligent grid allows to integrate many data source to drive analytics which transform data into useful information to support advanced operational control and strategic decision making. To realize an intelligent grid, it is necessary, firstly, capturing Knowledge, transforming data in information and introducing the knowledge in ICT framework and in Real-Time Systems. This is the right way to have a set of target and suitable information by using to take a correct decision, especially in real-time problem. So, in this work XBASE Cognitive Mapping Tool is presented. This tool allows to develop an intelligent grid, to support and “automate” strategic decision and so, to solve, also in real-time, every kind of problems. In particular, an application of this tool is presented, in monitoring of wastewater, the “BATTLE” Project.

Manifold alignment has been found to be useful in many fields of machine learning and data mining. In this paper we summarize our work in this area and introduce a general framework for manifold alignment. This framework generates a family of approaches to align manifolds by simultaneously matching the corresponding instances and preserving the local geometry of each given manifold. Some approaches like semi-supervised alignment and manifold projections can be obtained as special cases. Our framework can also solve multiple manifold alignment problems and be adapted to handle the situation when no correspondence information is available. The approaches are described and evaluated both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our methods including identification of topics shared by multiple document collections, and biological structure alignment are discussed in the paper.

International law is a singular exception to the top-down systems of law within nations. It presents the puzzle of how the law can be created or changed in the absence of authoritative rule-making institutions. The present paper is part of a work in progress that locates the law-making apparatus of international law in a complex adaptive system. Herein the focus is on thresholds. The first and most detailed threshold describes the emergence of the complex adaptive system. The second threshold consists of the transformation of international law from the voluntary to the automatic. The third threshold is here but has not yet been crossed: actualizing human rights as enforceable claims by individuals against States.

Long-term health monitoring is becoming increasingly important with the rising prevalence of chronic disease in the U.S. While many researchers are investigating the use of remote biological monitoring and telemedicine technologies, the use of frequent self-report in long-term health monitoring remains a relatively unstudied area. We discuss some of the many cognitive, affective and contextual issues that must be addressed in maintaining a long-term stream of quality data from patients at home or in the field, and how many of these issues can be addressed through the use of conversational agents.

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.

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.

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.