The Laplacian Eigenmap is a popular method for non-linear dimension reduction and data representation. This graph based method uses a Graph Laplacian matrix that closely approximates the Laplace-Beltrami operator which has properties that help to learn the structure of data lying on Riemaniann manifolds. However, the Graph Laplacian used in this method is derived from an intermediate graph that is built using local neighborhood information. In this paper we show that it possible to encapsulate global information represented by a Minimum Spanning Tree on the data set and use it for effective dimension reduction when local information is limited. The ability of MSTs to capture intrinsic dimension and intrinsic entropy of manifolds has been shown in a recent study. Based on that result we show that the use of local neighborhood and global graph can preserve the locality of the manifold. The experimental results validate the simultaneous use of local and global information for non-linear dimension reduction.

Agent adaptability — the ability of agents to change behavioral strategies when it is beneficial to do so — is presumed to be an important part of the robustness of complex adaptive systems (CAS). But, determining when changing behaviors is advantageous for agents has proven quite challenging in CAS research, as sometimes behavioral change is necessary, but other times it can impose costs that exceed benefits. I present the results from experiments using an agent-based model (ABM) designed to discover thresholds after which behavioral flexibility leads to improved societal-level outcomes in groups of agents in dynamic environments. The first major result is that there are thresholds in both levels of flexibility in agent behavior and in levels of turbulence in the environment below and above which there are marked differences in utility gains for agents. In particular, relatively high flexibility leads to lower overall utility scores, as well as, surprisingly, decreased diversity and increased inequality between agents. The second result is that at very high levels of environmental turbulence, the effects of the environment alone on agent utility overshadow any benefits to agents from flexible behavior strategies. This suggests, counter-intuitively, that the best strategy for agents in very dynamic environments is simply to keep behavior constant. The third major result is that there is an interaction between agent behavior and the environment: high flexibility of other agents can effectively make an environment more "dynamic", which just fuels more flexibility, and leads to a scramble between different strategies with no utility gain. A final theoretical contribution of the paper is that the model is able to show drawbacks to flexibility without relying on costs to changing behavior, as is done in much of the literature on strategy change.

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%.

Simulation techniques used to generate complex biological models are becoming promising research tools in oncology. Using a general Complex Adaptive Systems model that can be tailored to map various phenomena, here, we describe how this model applies to tumor growth. The multi-agent modeling environment is generated using Netlogo. The stochastic model consists of active objects including normal immune and cancer cells. The simulations conducted mimicked the tumor progression success and failure and the status of the tumor mass despite constant variations remained stable for an extended time. Furthermore, increasing the efficiency of the immune cells led to decreases in tumor cell numbers variable in both occurrence time and duration.

It has been hypothesized that to maximize learning, intelligent tutoring systems should detect and respond to both cognitive student states, and affective and metacognitive states such as uncertainty. In intelligent tutoring research so far, student state detection is primarily based on information available from a single student-system exchange unit, or turn. However, the features used in the detection of such states may have a temporal component, spanning multiple turns, and may change throughout the tutoring process. To test this hypothesis, an interactive tool was implemented for the visual analysis of prosodic features across a corpus of student turns previously annotated for uncertainty. The tool consists of two complementary visualization modules. The first module allows researchers to visually mine the feature data for patterns per individual student dialogue, and form hypotheses about feature dependencies. The second module allows researchers to quickly test these hypotheses on groups of students through statistical visual analysis of feature dependencies. Results show that significant differences exist among feature patterns across different student groups. Further analysis suggests that feature patterns may vary with student domain knowledge.

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.

Design-based learning (DBL) has many affordances for promoting deep and lasting learning of both content and complex skills. However, careful orchestration and scaffolding are usually needed to achieve its full potential. In this paper, we describe our efforts at implementing a software suite to meet the cognitive and meta-cognitive needs of learners engaged in DBL. In Study 1, our software suite gave learners the opportunity to design in simulation, to run experiments to learn the effects of variables, and it scaffolded science explanation construction. Through our analysis of study 1 we identified both cognitive and metacognitive needs that the software did not provide for. To meet these additional requirements, we added an interactive science resource and a case library to the software to provide multi-representational content material, to facilitate exploration, and to invite metacognitive reflection needed to do well at learning through design. Learners recognized what they did not understand, took initiative to explore those science concepts, and applied them in novel ways. We present here our analysis of the kinds of metacognitive help learners need to productively learn from design activities and some ways of providing that help. Our conclusion is that cognitive aid without related metacognitive aid is insufficient in a DBL environment.

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.