We believe that the flexibility and robustness of common sense reasoning comes from analogical reasoning, learning, and generalization operating over massive amounts of experience. Million-fact knowledge bases are a good starting point, but are likely to be orders of magnitude smaller, in terms of ground facts, than will be needed to achieve human-like common sense reasoning. This paper describes the FIRE reasoning engine which we have built to experiment with this approach. We discuss its knowledge base organization, including coarse-coding via mentions and a persistent TMS to achieve efficient retrieval while respecting the logical environment formed by contexts and their relationships in the KB. We describe its stratified reasoning organization, which supports both reflexive reasoning (Ask, Query) and deliberative reasoning (Solve, HTN planner). Analogical reasoning, learning, and generalization are supported as part of reflexive reasoning. To show the utility of these ideas, we describe how they are used in the Companion cognitive architecture, which has been used in a variety of reasoning and learning experiments.

Today millions of web-users express their opinions about many topics through blogs, wikis, fora, chats and social networks. For sectors such as e-commerce and e-tourism, it is very useful to automatically analyze the huge amount of social information available on the Web, but the extremely unstructured nature of these contents makes it a difficult task. SenticNet is a publicly available resource for opinion mining built exploiting AI and Semantic Web techniques. It uses dimensionality reduction to infer the polarity of common sense concepts and hence provide a public resource for mining opinions from natural language text at a semantic, rather than just syntactic, level.

We present an approach for acquiring common sense knowledge from social interaction. We argue that social common sense should be learned from daily interactions using implicit user's feedbacks and requires shared understanding of social situations. A service-oriented architecture, inspired from cognitive science, that foster mutual understanding between a smart environment and its inhabitants is presented. The method makes use of ConceptNet to work with common sense knowledge. We are able to successfully use and learn common sense knowledge.

Reasoning about Commonsense knowledge poses many problems that traditional logical inference doesn't handle well. Among these is cross-domain inference: how to draw on multiple independently produced knowledge bases. Since knowledge bases may not have the same vocabulary, level of detail, or accuracy, that inference should be "scruffy." The AnalogySpace technique showed that a factored inference approach is useful for approximate reasoning over noisy knowledge bases like ConceptNet. A straightforward extension of factored inference to multiple datasets, called Blending, has seen productive use for commonsense reasoning. We show that Blending is a kind of Collective Matrix Factorization (CMF): the factorization spreads out the prediction loss between each dataset. We then show that blending additional data causes the singular vectors to rotate between the two domains, which enables cross-domain inference. We show, in a simplified example, that the maximum interaction occurs when the magnitudes (as defined by the largest singular values) of the two matrices are equal, confirming previous empirical conclusions. Finally, we describe and mathematically justify Bridge Blending, which facilitates inference between datasets by specifically adding knowledge that "bridges" between the two, in terms of CMF.

In order to understand the role of metacognition and self-regulation in student learning, 35 college students were asked to solve problems in college linear algebra and in remedial math using Cognitive Constructor. Results reveal the predominance of forward chaining in problem solving.

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.

A well-documented finding in the help-seeking literature is that especially those learners who need it the most do not seek help (appropriately). In this exploratory study, we investigated re-reading as a unique window into elementary help-seeking processes. Students had to learn the content of multiple hypertext pages of different complexity for a subsequent knowledge test. After this learning phase we randomly assigned learners to two experimental groups: The memory control group (MG, n = 14) directly answered the knowledge test and the experimental help-seeking group (HSG, n = 15) had the option to re-read the hypertext pages before answering. Results show that HSG students outperformed MG students and that HSG students strongly adapted the extent and frequency of their re-reading to task complexity and the complexity of the hypertext pages. However, more re-reading or more adaptivity did not automatically enhance performance on the knowledge test. The implications of these findings for the design of adaptive (metacognitive) help functions in computer-based learning environments will be discussed.

Meta-cognitive skills to be developed in learning for the 21st century is the detection and correction of errors in solutions. These meta-cognitive skills can help to detect errors the learner has made her/himself as well as errors others have made. Our investigations in learning from errors have the ultimate goal to adapt the selection and presentation to the learner so that he/she can better learn from erroneous examples others have made. In our experiments we found that (1) erroneous examples with help provision can promote students skill of find errors, (2) the benefit from erroneous examples depends on the relation between the student's level and the example's difficulty, i.e. if the student is prepared for the problem, (3) for many students it is very difficult to correct errors.

Learners that have better metacognition acquire knowledge faster than others who do not. If we had better models of such learning, we would be able to build a better metacognitive educational system. In this paper, we propose a computational model that uses a probabilistic context free grammar induction algorithm yielding metacognitive learning by acquiring deep features to assist future learning. We discuss the challenges of integrating this model into a synthetic student, and possible future studies in using this model to better understand human learning. Preliminary results suggest that both stronger prior knowledge and a better learning strategy can speed up the learning process. Some model variations generate human-like error pattern.

Developing the capabilities of children to comprehend written texts is key to their development as young adults. Text comprehension skills develop enormously from the age of 7- 8 until the age of 11. Nowadays, several young children (˜5% – 10% of novice readers) turn out to be poor (text) comprehenders: they demonstrate text comprehension difficulties, related to inference-making skills, despite proficiency in lowlevel cognitive skills like word decoding. Though there are several pencil-and-paper reading interventions for improving inference-making skills on text, and addressed to poor comprehenders, the design and evaluation of Adaptive Learning Systems (ALSs) are lagging behind. The use of more intelligent ALSs to custom-tailor such interventions in the form of games for poor comprehenders has tremendous potential. Our system embodies that potential. This paper presents the design of our ALS by focusing on its intelligent adaptive engine and the related conceptual models, and by presenting the visual interfaces for story telling and gaming.