The relationship between equilibrium convergence to a uniform quality distribution and price is investigated in the Q-model, a self-organizing, evolutionary computational matching model of a fixed-price post-secondary higher education created by Ortmann and Slobodyan (2006). The Q-model is replicated with price equaling 100% its Ortmann and Slobodyan (2006) value, Varying the fixed price between 0% and 200% reveals thresholds at which the Q-model reaches different market clustering configurations. Results indicate structural market robustness to prices less than 100% and high sensitivity to prices greater than 100%.

Uncertainty in natural language dialogue is often treated through stochastic models. Some of the authors already presented TutorJ that is an Intelligent Tutoring System, whose interaction with the user is very intensive, and makes use of both dialogic and graphical modality. When managing the interaction, the system needs to cope with uncertainty due to the understanding of the user's needs and wishes. In this paper we present the extended version of TutorJ, focusing on the new features added to its chatbot module. These features allow to merge deterministic and probabilistic reasoning in dialogue management, and in writing the rules of the system's procedural memory.

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.

The web forum is a key tool in the building of new knowledge among students in Learning Management Systems. Students’ posted messages, in fact, build up a relationship network which supports a collaborative reflection about the forum topic. In this network two interaction levels can be distinguished. The former is the interaction between peers (the students), the latter between students and instructors (teachers and tutors). The role of the second interaction is particularly important as a feedback mechanism in the discussion dynamic but it is subjected to two kinds of limitations. The first one is the huge number of messages that makes difficult, for tutors and teachers, to quickly evaluate the progress of their students and the second one is the subjective bias of the tutors that influence the evaluation. In order to limit these two inefficiencies a multiagent system can be used to monitor such evolution and recognize the state in which the forum is. Such system is based on metrics derived from the textual and social network analysis that, feeding a rule engine, gives the instructor a more objective view of the forum evolution.

Learning about complex and challenging science topics with advanced learning technologies requires students to regulate their learning. The deployment of key cognitive and metacognitive regulatory processes is key to enhancing learning in open-ended learning environments such as hypermedia. In this paper, we propose a metaphor—Computers as MetaCognitive tools—to characterize the complex nature of the learning context, self- regulatory processes, task conditions, and features of advanced learning technologies. We briefly outline the theoretical and conceptual assumptions of self-regulated learning (SRL) underlying MetaTutor, a hypermedia environment designed to train and foster students’ SRL processes in biology. Lastly, we provide preliminary learning outcome and SRL process data on the deployment of SRL processes during learning with MetaTutor.

The current "Cognitive Systems" initiative under the Euro­pean Commission's "7th Framework Programme for Re­search and Technological Development (FP7)" originated shortly after the turn of the millenium when, under the heading of "Cognitive Vision", a set of nine projects was selected for funding. Its general aim is to give a new im­petus to (1) strengthening the scientific foundation for en­gineering artificial cognitive systems - i.e., artificial sys­tems that perceive and (inter-) act, based on a suitable un­derstanding of their environment; and thus to provide the ground for (2) advancing or creating enabling technologies for a variety of applications involving interaction within all sorts of environment. These pertain to, for instance, but not exclusively, robotics, assistive technologies, and language and vision based man-machine interfaces. As of November 2009 the "Cognitive Systems, Interaction, and Robotics" portfolio comprises some 100 projects, finished and ongo­ing ones, representing almost 300 MEuro in funding. This talk addresses the background, rationale and context of the European "Cognitive Systems" initiative. It high­lights key issues, current achievements and possible future directions.

GLAIR (Grounded Layered Architecture with Integrated Reasoning) is a multi-layered cognitive architecture for embodied agents operating in real,virtual, or simulated environments containing other agents. The highest layer of the GLAIR Architecture, the Knowledge Layer (KL), contains the beliefs of the agent, and is the layer in which conscious reasoning, planning, and act selection is performed. The lowest layer of the GLAIR Architecture, the Sensori-Actuator Layer (SAL), contains the controllers of the sensors and effectors of the hardware or software robot. Between the KL and the SAL is the Perceptuo-Motor Layer (PML), which grounds the KL symbols in perceptual structures and subconscious actions, contains various registers for providing the agent's sense of situatedness in the environment, and handles translation and communication between the KL and the SAL. The motivation for the development of GLAIR has been "Computational Philosophy", the computational understanding and implementation of human-level intelligent behavior without necessarily being bound by the actual implementation of the human mind. Nevertheless, the approach has been inspired by human psychology and biology.

Thought is an essential aspect of mental function, but remains very poorly understood. In this paper, we take the view that thought is a response process — the emergent and dynamic configuration of structured response, i.e., ideas, by composing response elements, i.e., concepts, from a repertoire under the influence of afferent information, internal modulation and evaluative feedback. We hypothesize that the process of generating ideas occurs at two levels: 1) The identification of a context-specific subset — or workspace — of concepts from the larger repertoire; and 2) The configuration of plausible/useful ideas within this workspace. Workspace configuration is mediated by a dynamic selector network (DSN), which is an internal attention/working memory system. Each unit of the DSN selectively gates a subset of concepts, so that any pattern of activity in the DSN defines a workspace. The configuration of efficient and flexible workspaces is mediated by dynamical structures termed graded attractors — attractors where the set of active units can be varied in systematic order by inhibitory modulation. A graded attractor in the DSN can project a selective bias — a ``searchlight" — onto the concept repertoire to define a specific workspace, and inhibitory modulation can be used to vary the breadth of this workspace. As it experiences various contexts, the cognitive system can configure a set of graded attractors, each covering a domain of similar contexts. In this paper, we focus on a mechanism for configuring context-specific graded attractors, and evaluate its performance over a set of contexts with varying degrees of similarity. In particular, we look at whether contexts are clustered appropriately into a minimal number of workspaces based on the similarity of the responses they require. While the focus in this paper is on semantic workspaces, the model is broadly applicable to other cognitive response functions such as motor control or memory recall.

The concept of 'self' has been investigated using many methodologies (e.g. the philosophical approach and the neurobiological approach) that has given rise to issues that yielded popular debates. In this paper, I endeavor to employ virtual reality as a new tool for investigating 'self'. Future directions are provided that could be further helpful in advancing our understanding about the self amidst the complexity of culture.

A self-organizing neural network is described that can associate between different modalities and also has the ability to learn perceptual sequences. This architecture is a step towards the development of a complete agent containing simplified versions of all major neural subsystems in a mammal. It aims at exploring as well as takes inspiration from the idea that cognitive function involves an internal simulation of perception and movement. We have tested the architecture in simulations as well as together with real sensors with very encouraging results.