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Goel, Ashok K.

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Now showing 1 - 10 of 13
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    A virtual coach for question asking and enabling learning by reflection in startup engineering
    (Georgia Institute of Technology, 2020-12) Goel, Ashok K. ; Hong, Sung Jae ; Kuthalam, Mukundan ; Arcalgud, Arup ; Gulati, Siddharth ; Howe, James ; Karnati, Nikhita ; Mardis, Aaron ; Ro, Jae ; McGreggor, Keith
    The Socratic method of teaching engages learners in extended conversations and encourages learning through answering questions, making arguments, and reflecting on the evolving conversation. This method can be a powerful instrument of learning by reflection, especially in domains in which the right answers to open questions are not known in advance such as entrepreneurship. In this paper, we describe an initial experiment in developing AI technology for simulating the Socratic method of teaching in learning about entrepreneurship. When a would-be entrepreneurs creates a business model on the Business Model Canvas (BMC), the AI agent named Errol uses semantic and lexical analysis of the entries on the BMC to ask questions of the students. By attempting to categorize and correct the errors that novices typically make, Errol seeks to accelerate the process by which a novice can start creating more expert-like business models
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    Jill Watson: A Virtual Teaching Assistant for Online Education
    (Georgia Institute of Technology, 2016) Goel, Ashok K. ; Polepeddi, Lalith
    MOOCs are rapidly proliferating. However, for many MOOCs, the effectiveness of learning is questionable and student retention is low. One recommendation for improving the learning and the retention is to enhance the interaction between the teacher and the students. However, the number of teachers required to provide learning assistance to all students enrolled in all MOOCs is prohibitively high. One strategy for improving interactivity in MOOCs is to use virtual teaching assistants to augment and amplify interaction with human teachers. We describe the use of a virtual teaching assistant called Jill Watson (JW) for the Georgia Tech OMSCS 7637 class on Knowledge-Based Artificial Intelligence. JW has been operating on the online discussion forums of different offerings of the KBAI class since Spring 2016. By now some 750 students have interacted with different versions of JW. In the latest, Spring 2017 offering of the KBAI class, JW autonomously responded to student introductions, posted weekly announcements, and answered routine, frequently asked questions. In this article, we describe the motivations, background, and evolution of the virtual question-answering teaching assistant.
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    Goal Reasoning: Papers from the ACS Workshop
    (Georgia Institute of Technology, 2015-05-28) Aha, David W. ; Anderson, Tory S. ; Bengfort, Benjamin ; Burstein, Mark ; Cerys, Dan ; Coman, Alexandra ; Cox, Michael T. ; Dannenhauer, Dustin ; Floyd, Michael W. ; Gillespie, Kellen ; Goel, Ashok K. ; Goldman, Robert P. ; Jhala, Arnav ; Kuter, Ugur ; Leece, Michael ; Maher, Mary Lou ; Martie, Lee ; Merrick, Kathryn ; Molineaux, Matthew ; Muñoz-Avila, Héctor ; Roberts, Mark ; Robertson, Paul ; Rugaber, Spencer ; Samsonovich, Alexei ; Vattam, Swaroop S. ; Wang, Bing ; Wilson, Mark
    This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013.
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    An Experiment in Teaching Cognitive Systems Online
    (Georgia Institute of Technology, 2015) Goel, Ashok K. ; Joyner, David A.
    In Fall 2014 we offered an online course CS 7637 Knowledge-Based Artificial Intelligence: Cognitive Systems (KBAI) to about 200 students as part of the Georgia Tech Online MS in CS program. We incorporated lessons from learning science into the design of the project-based online KBAI course. We embedded ~150 microexercises and ~100 AI nanotutors into the online videos. As a quasi-experiment, we ran a typical inperson class with 75 students in parallel, with the same course syllabus, structure, assignments, projects and examinations. Based on the feedback of the students in the online KBAI class, and comparison of their performance with the students in the inperson class, the online course appears to have been a success. In this paper, we describe the design, development and delivery of the online KBAI class. We also discuss the evaluation of the course.
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    SoD-TEAM: Teleological reasoning in adaptive software design
    (Georgia Institute of Technology, 8/31/2012) Goel, Ashok K. ; Rugaber, Spencer
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    Thinking in Pictures as a Cognitive Account of Autism
    (Georgia Institute of Technology, 2010) Kunda, Maithilee ; Goel, Ashok K.
    We analyze the hypothesis that some individuals on the autism spectrum may use visual mental representations and processes to perform certain tasks that typically developing individuals perform verbally. We present a framework for interpreting empirical evidence related to this “Thinking in Pictures” hypothesis and then provide comprehensive reviews of data from several different cognitive tasks, including the /n/-back task, serial recall, dual task studies, Raven’s Progressive Matrices, semantic processing, false belief tasks, visual search and attention, spatial recall, and visual recall. We also discuss the relationships between the Thinking in Pictures hypothesis and other cognitive theories of autism including Mindblindness, Executive Dysfunction, Weak Central Coherence, and Enhanced Perceptual Functioning.
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    Interactive Story Authoring for Knowledge‐Based Support for Investigative Analysis: A Second STAB at Making Sense of VAST Data
    (Georgia Institute of Technology, 2010) Goel, Ashok K. ; Sinharoy, Avik ; Adams, Summer ; Dokania, Adity
    The sensemaking task in investigative analysis generates stories that connect entities and events in an input stream of data. The Stab system represents crime stories as hierarchical scripts with goals and states. It generates multiple stories as explanatory hypotheses for an input data stream containing interleaved sequences of events, recognizes intent in a specific event sequence, and calculates confidence values for the generated hypotheses. In this report, we describe Stab2, a new interactive version of the knowledge‐based Stab system. Stab2 contains a story editor that enables users to enter and edit crime stories. We illustrate Stab2 with examples from the IEEE VAST contest datasets.
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    Playing Detective: Using AI for Sensemaking in Investigative Analysis
    (Georgia Institute of Technology, 2009) Goel, Ashok K. ; Adams, Summer ; Cutshaw, Neil ; Sugandh, Neha
    The sensemaking task in investigative analysis generates models that connect entities and events in an input stream of data. We describe two knowledge systems for aiding sensemaking in investigative analysis. The Spade system uses crime schemas to generate an explanatory hypothesis and past cases to validate the hypothesis. The STAB system represents crime schemas as hierarchical scripts with goals and states. It generates multiple explanatory hypotheses for an input data stream containing interleaved sequences of events, recognizes intent in a specific event sequence, and calculates confidence values for the generated hypotheses. We view STAB and Spade as automated cognitive assistants to human analysts: they may support sensemaking in investigative analysis by generating and managing multiple competing hypotheses.
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    Biologically-Inspired Innovation in Engineering Design: a Cognitive Study
    (Georgia Institute of Technology, 2007) Vattam, Swaroop ; Helms, Michael E. ; Goel, Ashok K.
    Biologically-inspired design uses analogous biological phenomena to develop solutions for engineering problems. Understanding, learning and practicing this approach to design is challenging because biologists and engineers speak different languages, have different perspectives on design, with different constraints on design problems and different resources for realizing an abstract design. In Fall 2006, we attended ME/ISyE/MSE/PTFe/BIOL 4803: Biologically-Inspired Design, an interdisciplinary introductory course for juniors and seniors offered at Georgia Tech. We collected course materials, took class notes, observed teacher-student and student-student interactions in the classroom. We also observed some sessions of a few interdisciplinary teams of students engaged in their design projects outside the classroom. We then analyzed the observations in terms of existing cognitive theories of design, modeling, and analogy. The goals of this cognitive study were to (1) understand the cognitive basis of biologically-inspired innovation in engineering design, (2) identify opportunities for enabling more effective learning of biologically-inspired design, and (3) examine the implications for developing computational tools for facilitating effective biologically-inspired design. This report summarizes our main observations about learning biologically-inspired design, and presents our preliminary analysis of biologically-inspired design in a classroom setting.
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    Using Spatial Structure in the Associative Retrieval of 2-D Line Drawings
    (Georgia Institute of Technology, 2002) Yaner, Patrick W. ; Goel, Ashok K.
    We consider the problem of associative image retrieval, focusing on retrieval of 2-D line drawings by example. We represent 2-D line drawings as semantic networks of spatial elements and relations among them. We describe a process for retrieving the drawings based on a structural analogy between the query and the stored images. We then present several methods of retrieving the drawings: the first family methods uses logical unification and resolution to accomplish the matching; the second family of methods heuristically prunes the stored drawings and then does the resolution and unification on the remaining drawings; two more methods treat the retrieval problem as a constraint satisfaction problem and use common CSP techniques for solving it; and the last two methods combine the heuristic step of the second method with the CSP technique of the third and fourth. We report on experimental results that compare the performance of these methods on computer-based libraries of drawings. A surprising result of our work is that for the fastest of these methods two stage retrieval appeared to offer no benefit over one stage retrieval.