Common sense collection has long been an important subfield of AI. This paper introduces a combined architecture for commonsense harvesting by text mining and a game with a purpose. The text miner module uses a seed set of known facts (sampled from ConceptNet) as training data and produces candidate commonsense facts mined from corpora. The game module taps humans' knowledge about the world by letting them play a simple slot-machine-like game. The proposed system allows us to collect significantly better commonsense facts than the state-of-the-art text miner alone, as shown experimentally for 5 rather different types of commonsense relations.

Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitive monitor the use of strategies and repeatedly make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.

A seminal study conducted by Greene, Bolick, and Robertson (2010) showed that learners do not always engage in appropriate metacognitive and self-regulatory processes while learning about history. However, little research exists to guide the design of technology-rich learning environments (TRLEs) as metacognitive tools in social sciences education. In order to address this issue, we designed a metacognitive tool using a bottom-up approach (Poitras, 2010; Poitras, Lajoie, & Hong, in prep). Thirty-two undergraduate students read an historical narrative text either with or without the benefit of the metacognitive tool. Results from process and product data suggest that learners had better recall because the metacognitive tool assisted learners to (a) notice that particular events are unexplained in the circumstances described in an historical narrative text, and (b) generate hypothetical causes to explain the occurrence of such events. We discuss the implications of these findings for the development of the MetaHistoReasoning Tool, a TRLE that assists learners’ historical reasoning while they accomplish authentic tasks of historical inquiry.

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.

The ability to take intelligent actions in real-world domains is a goal of great interest in the machine learning community. Unfortunately, the real-world is filled with systems that can bepartially observed but cannot, as yet, be described by first principlemodels. Moreover, the traditional paradigm of direct interaction with the environment used in reinforcement learning (RL) is often prohibitively expensive in practice. An alternative approach that simultaneously solves both of these problems is to gain experience in simulation; the simulation in this approach is a computational model derived from observations. Advances in sensory and information technology are simplifying the acquisition and distribution of real-world datasets to computational scientists; thus, the barrier to linking intelligent control with real-world domains is becoming one of identifying high-quality state-space and transition functions directly from observations. From a dynamical systems perspective, this barrier is analogous to the problem of finding high-quality manifold embeddings and a rich literature of theory and practice exists to address it. The contribution of this work is two-fold. First, we describe an approach for learning optimal control strategies directly from observations using manifold embeddings as the intermediate state representation. Second, we demonstrate how control strategies constructed in this way can answer important scientific questions. As a concrete example, we use our approach to guide experimental decisions in neurostimulation treatments of epilepsy.

What commonsense knowledge do intelligent systems need, in order to recover from failures or deal with unexpected situations? It is impractical to represent predetermined solutions to deal with every unanticipated situation or provide predetermined fixes for all the different ways in which systems may fail. We contend that intelligent systems require only a finite set of anomaly-handling strategies to muddle through anomalous situations. We describe a generalized metacognition module that implements such a set of anomaly-handling strategies and that in principle can be attached to any host system to improve the robustness of that system. Several implemented studies are reported, that support our contention.

In this paper, we present a method for isometric correction of manifold learning techniques. We first present an isometric nonlinear dimension reduction method. Our proposed method overcomes the issues associated with well-known isometric embedding techniques such as ISOMAP and maximum variance unfolding (MVU), i.e., computational complexity and the geodesic convexity requirement. Based on the proposed algorithm, we derive our isometric correction method. Our approach follows an isometric solution to the problem of local tangent space alignment. We provide a derivation of a fast iterative solution. The performance of our algorithm is illustrated on both synthetic and real datasets compared to other methods.

The paper uses the SCOP theory of concepts to model the role of environmental context on three levels of entrepreneurial opportunity: idea generation, idea development, and entrepreneurial decision. The role of contextual-fit in the generation and development of ideas is modeled as the collapse of their superposition state into one of the potential states that composes this superposition. The projection of this collapsed state on the socio-economic basis results in interference between the developed idea and the perceptions of the supporting community, undergoing an eventual collapse for an entrepreneurial decision that reflects the shared vision of its stakeholders. The developed idea may continue to evolve due to continuous or discontinuous changes in the environment. The model offers unique insights into the effects of external influences on entrepreneurial decisions.

This article introduces a "pseudo classical" notion of modelling non-separability. This form of non-separability can be viewed as lying between separability and quantum-like non-separability. Non-separability is formalized in terms of the non-factorizabilty of the underlying joint probability distribution. A decision criterium for determining the non-factorizability of the joint distribution is related to determining the rank of a matrix as well as another approach based on the chi-square-goodness-of-fit test. This pseudo-classical notion of non-separability is discussed in terms of quantum games and concept combinations in human cognition.

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).