This paper provides an overview of a web-based, database-driven cognitive support system for scaffolding ill-structured problem solving processes through fostering self-regulation. Self-regulation learning and ill-structured problem-solving theories guided the design framework of this cognitive tool. Of particular interest are the roles of question prompts, expert view, and peer review mechanisms in supporting self-monitoring, self-regulation, and self-reflection in the processes of ill-structured problem solving, which have been tested through empirical studies in various content domains and contexts. Based on findings, suggestions are made to improve the cognitive support system for future research, including mapping self-regulation learning processes more closely with ill-structured problem-solving processes, and focusing on the system’s capability to automatically adapt scaffolding based on individual needs and prior knowledge.

But moreover, the implicit nature Metacognitive activity is a core aspect of many multifaceted of metacognitive activities makes the goal of supporting practices, but supporting such activity in educational contexts metacognition perhaps an even larger challenge. When we is a complex endeavor. One example of such a practice think about the two major learning goals described above includes the substantive inquiry practices that different in the science inquiry example and other learning goals educational policy groups (for example, National Research put forth in many educational policies, we can the central Council 2000) recommend for K-12 student curricula, including challenge that we want to address with metacognitive support: those practices that involve more authentic types of (1) supporting novice learners to mindfully engage in scientific inquiry along with online inquiry activities that incorporate the metacognitive activity necessary to successfully participate a growing number of digital libraries and other in complex, multifaceted practices, and (2) supporting information resources. There are many characterizations novice learners to learn good metacognitive practiceswhat of inquiry, but we can succinctly describe inquiry as a set metacognitive activities are, why they are important, and of activities that involve: (1) asking and developing questions how to engage in them. Supporting metacognition is vital to investigate; (2) searching for and gathering relevant to essentially help make these implicit activities more explicit data and information; (3) reading, evaluating, and analyzing to learners, yet we continue to see how difficult it is to the gathered data and information; and (4) synthesizing provide such support.

The AAAI Fall Symposium on Meta-Cognitive Educational - What are the theoretical foundations and how are they articulated Systems: One Step Forward is the second edition of the successful in CBLEs? MCES implemented as CBLEs are designed to interact with - What are the main aspects of metacognition, selfregulation users, and support their learning and decision-making processes. Can MCES actually foster they need to plan their learning activities, to adapt their learners to be self-regulating agents? How can a MCES learning strategies to meet learning goals, become aware of be autonomous and increase its knowledge to match the changing task conditions, and the dynamic aspects of the learners evolving skills and knowledge? MCES may not be embodied, prior to, during, and after they have been involved in but does it help if they act as intentional agents? the learning environment.

While the application areas addressed typically - Social Interaction operate at a macroscopic scale and could not be considered quantum in a quantum mechanical sense, they may - Finance, economics, and social structures (e.g., organizations, share many key properties with quantum systems. Each paper was thoroughly reviewed by at problems with AI in non-quantum domains more efficiently least three members of the international programme committee. Kanerva (Stanford University), and an invited talk on day - Logic, planning, agents and multi-agent systems 2 by Terry Bollinger (ONR/MITRE). Finally, welcome and we look forward to a stimulating symposium!

In order to better understand the mechanisms that lead to resiliency in natural systems, to support decisions that lead to greater resiliency in systems we effect, and to create models that will utilized in highly resilient systems, methods for resiliency analysis will be required. Existing methods and technology for robustness analysis provide a foundation for a rigorous approach to resiliency analysis, but extensions are necessary to address the multiple time scales that must be modeled to understand highly adaptive systems. Further, if resiliency modeling is to be effective, it must be contextualized, requiring that the supporting software will need to mirror the systems being modeling by being pace layered and adaptive.

Quantum theory has recently been employed to further advance thetheory of information retrieval (IR). A challenging research topicis to investigate the so called quantum-like interference in users'relevance judgment process, where users are involved to judge therelevance degree of each document with respect to a given query. Inthis process, users' relevance judgment for the current document isoften interfered by the judgment for previous documents, due to theinterference on users' cognitive status. Research from cognitivescience has demonstrated some initial evidence of quantum-likecognitive interference in human decision making, which underpins theuser's relevance judgment process. This motivates us to model suchcognitive interference in the relevance judgment process, which inour belief will lead to a better modeling and explanation of userbehaviors in relevance judgement process for IR and eventually leadto more user-centric IR models. In this paper, we propose to useprobabilistic automaton (PA) and quantum finite automaton (QFA),which are suitable to represent the transition of user judgmentstates, to dynamically model the cognitive interference when theuser is judging a list of documents.

A key concept in many Information Retrieval (IR) tasks, e.g. document indexing, query language modelling, aspect and diversity retrieval, is the relevance measurement of topics, i.e. to what extent an information object (e.g. a document or a query) is about the topics. This paper investigates the interference of relevance measurement of a topic caused by another topic. For example, consider that two user groups are required to judge whether a topic q is relevant to a document d, and q is presented together with another topic (referred to as a companion topic). If different companion topics are used for different groups, interestingly different relevance probabilities of q given d can be reached. In this paper, we present empirical results showing that the relevance of a topic to a document is greatly affected by the companion topic’s relevance to the same document, and the extent of the impact differs with respect to different companion topics. We further analyse the phenomenon from classical and quantum-like interference perspectives, and connect the phenomenon to nonreality and contextuality in quantum mechanics. We demonstrate that quantum like model fits in the empirical data, could be potentially used for predicting the relevance when interference exists.

In multimedia information retrieval, where a document may contain both textual and visual content features, the ranking of documents is often computed by heuristically combining the feature spaces of different media types or combining the ranking scores computed independently from different feature spaces. In this paper, we propose a principled approach inspired by quantum theory. Specifically, we propose a tensor product based model aiming to represent textual and visual content features of an image as a non-separable composite system. The ranking scores of the images are then computed in the form of a quantum measurement. In addition, the correlations between features of different media types are incorporated in the framework. Experiments on ImageClef2007 show a promising performance of the tensor based approach.

A totally new computational grammatical structure has been developed which encompasses the general class of self-organizing systems. It is based on a universal rewrite system and the principle of nilpotency, where a system and its environment have a space-time variation defined by the phase, which preserves the dual mirror-image relationship between the two. As briefly summarized in the paper, there is already substantial and hard evidence in favour of the application of this universal rewrite approach to quantum physics. Further applications in diverse fields suggest that, while the relationship between a self-organized system and its environment must be fully understood in quantum physical computational terms rather than digital ones, a new discrete approach to this quantum mechanical understanding can be described, which extends beyond the purely quantum range. It offers a new calculational means, within existing digital computational technology, to approach and validate the workings of self-organized systems, and may well encompass related but different computational methods used by other workers.

In this article we describe a cognitive heuristic known as the unpacking effect by using a mathematical model, based on the quantum formalism, already introduced for the conjunction fallacy. We present the basic postulates of such quantum-like model and we show that the presence of interference terms is responsible of the unpacking effect. In particular, the sign of the interference and its functional form are able to describe the experimental results about subadditivity, superadditivity and additivity. A comparison with previous models is presented, as well as new experimental predictions, allowing to conclude that this new formalism and the basic concepts of quantum information processing provide a new promising way to describe and understand human judgement and categorization.