Verbal analogy problem sets: An inventory of testing materials.

Analogical reasoning is an active topic of investigation across education, artificial intelligence (AI), cognitive psychology, and related fields. In all fields of inquiry, explicit analogy problems provide useful tools for investigating the mechanisms underlying analogical reasoning. Such sets have been developed by researchers working in the fields of educational testing, AI, and cognitive psychology. However, these analogy tests have not been systematically made accessible across all the relevant fields. The present paper aims to remedy this situation by presenting a working inventory of verbal analogy problem sets, intended to capture and organize sets from diverse sources

Learning strategies in interpreting text: From comprehension to illustration

Learning strategies can be described as behaviours and thoughts a learner engages in during learning that are aimed at gaining knowledge. Learners are, to use Mayer’s (1996) constructivist definition, ‘sense makers’. We can therefore position this to mean that, if learners are sense makers, then learning strategies are essentially cognitive processes used when learners are striving to make sense out of newly presented material. This paper intends to demonstrate that such thoughts and behaviours can be made explicit and that students can co-ordinate the basic cognitive processes of selecting, organising and integrating. I will discuss two learning strategies which were developed during three cycles of an action research enquiry with a group of illustration students. While each cycle had its own particular structure and aims, the main task, that of illustrating a passage of expository text into an illustration was a constant factor. The first learning strategy involved assisting students develop ‘macropropositions’—personal understandings of the gist or essence of a text (Louwerse and Graesser, 2006; Armbruster, Anderson and Ostertag, 1987; Van Dijk & Kintsch, 1983). The second learning strategy used a form of induction categorised as analogical reasoning (Holyoak, 2005; Sloman and Lagnado, 2005). Both strategies were combined to illustrate the expository text extract. The data suggests that design students benefit from a structured approach to learning, where thinking processes and approaches can be identified and accessible for other learning situations. The research methodology is based on semi-structured interviews, questionnaires, developmental design (including student notes) and final design output. All student names used are pseudonyms. The text extract from ‘Through the Magic Door’ an essay Sir Arthur Conan Doyle, (1907) has been included as it provides context to analysis outcomes, student comments and design outputs. Keywords: Action Research; Illustration; Macrostructures; Analogical Reasoning; Learning Strategies</p

Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning

A robot that can be simply told in natural language what to do -- this has been one of the ultimate long-standing goals in both Artificial Intelligence and Robotics research. In near-future applications, robotic assistants and companions will have to understand and perform commands such as set the table for dinner'', make pancakes for breakfast'', or cut the pizza into 8 pieces.'' Although such instructions are only vaguely formulated, complex sequences of sophisticated and accurate manipulation activities need to be carried out in order to accomplish the respective tasks. The acquisition of knowledge about how to perform these activities from huge collections of natural-language instructions from the Internet has garnered a lot of attention within the last decade. However, natural language is typically massively unspecific, incomplete, ambiguous and vague and thus requires powerful means for interpretation. This work presents PRAC -- Probabilistic Action Cores -- an interpreter for natural-language instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot. To this end, PRAC formulates the problem of instruction interpretation as a reasoning problem in first-order probabilistic knowledge bases. In particular, the system uses Markov logic networks as a carrier formalism for encoding uncertain knowledge. A novel framework for reasoning about unmodeled symbolic concepts is introduced, which incorporates ontological knowledge from taxonomies and exploits semantically similar relational structures in a domain of discourse. The resulting reasoning framework thus enables more compact representations of knowledge and exhibits strong generalization performance when being learnt from very sparse data. Furthermore, a novel approach for completing directives is presented, which applies semantic analogical reasoning to transfer knowledge collected from thousands of natural-language instruction sheets to new situations. In addition, a cohesive processing pipeline is described that transforms vague and incomplete task formulations into sequences of formally specified robot plans. The system is connected to a plan executive that is able to execute the computed plans in a simulator. Experiments conducted in a publicly accessible, browser-based web interface showcase that PRAC is capable of closing the loop from natural-language instructions to their execution by a robot

Captured by associations : semantic distractibility during analogical reasoning in schizophrenia

Impaired cognitive control, for instance increased distractibility in simple conflict tasks such as Stroop, is considered one of fundamental cognitive deficits in schizophrenia patients. Relatively less is known about patients proneness to distraction in more complex, longer-lasting cognitive tasks. We applied the four-term analogies with and without distraction to 51 schizophrenia patients in order to examine whether they display increased distractibility during analogical reasoning, and to test which kind of distractors (semantic, categorical, or perceptual) elicits their strongest distraction, as compared to 51 matched controls. We found that (a) both groups reasoned by analogy comparably well when distraction was absent; (b) in both groups distraction significantly decreased performance; (c) schizophrenia patients were significantly more distracted than the controls; (d) in both groups the semantic distractors were selected more frequently than the categorical distractors, while the perceptual distractors were virtually ignored; as well as (e) in both groups distractibility in the four-term analogies was unrelated with distractibility in the simple perceptual conflict task, suggesting that these two distraction types tap into different cognitive mechanisms. Importantly, a significantly stronger distractibility in the schizophrenia group could not be explained by their lower intelligence, because the two groups were matched on the fluid reasoning test. We conclude that during complex cognitive processing schizophrenia patients become captured by irrelevant semantic associations. The patients are also less willing to critically evaluate their responses

Multimodal Analogical Reasoning over Knowledge Graphs

Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance. Code and datasets are available in https://github.com/zjunlp/MKG_Analogy.Comment: Accepted by ICLR 202

Assessing schematic knowledge of introductory probability theory

[Abstract]: The ability to identify schematic knowledge is an important goal for both assessment and instruction. In the current paper, schematic knowledge of statistical probability theory is explored from the declarative-procedural framework using multiple methods of assessment. A sample of 90 undergraduate introductory statistics students was required to classify 10 pairs of probability problems as similar or different; to identify whether 15 problems contained sufficient, irrelevant, or missing information (text-edit); and to solve 10 additional problems. The complexity of the schema on which the problems were based was also manipulated. Detailed analyses compared text-editing and solution accuracy as a function of text-editing category and schema complexity. Results showed that text-editing tends to be easier than solution and differentially sensitive to schema complexity. While text-editing and classification were correlated with solution, only text-editing problems with missing information uniquely predicted success. In light of previous research these results suggest that text-editing is suitable for supplementing the assessment of schematic knowledge in development

Flexibly Instructable Agents

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible instructability that distinguish it from previous systems: (1) it can take known or unknown commands at any instruction point; (2) it can handle instructions that apply to either its current situation or to a hypothetical situation specified in language (as in, for instance, conditional instructions); and (3) it can learn, from instructions, each class of knowledge it uses to perform tasks.Comment: See http://www.jair.org/ for any accompanying file