Design, innovation and case-based reasoning.

The design task is especially appropriate for applying, integrating, exploring and pushing the boundaries of case-based reasoning. In this paper, we briefly review the challenges that design poses for case-based reasoning and survey research on case-based design ranging from early explorations to more recent work on innovative design. We also summarize the theoretical contributions this research has made to case-based reasoning itself

Mutual Theory of Mind for Human-AI Communication

From navigation systems to smart assistants, we communicate with various AI on a daily basis. At the core of such human-AI communication, we convey our understanding of the AI's capability to the AI through utterances with different complexities, and the AI conveys its understanding of our needs and goals to us through system outputs. However, this communication process is prone to failures for two reasons: the AI might have the wrong understanding of the user and the user might have the wrong understanding of the AI. To enhance mutual understanding in human-AI communication, we posit the Mutual Theory of Mind (MToM) framework, inspired by our basic human capability of "Theory of Mind." In this paper, we discuss the motivation of the MToM framework and its three key components that continuously shape the mutual understanding during three stages of human-AI communication. We then describe a case study inspired by the MToM framework to demonstrate the power of MToM framework to guide the design and understanding of human-AI communication.Comment: 7 pages, 4 figure

Hysteresis Models for Steel Members Subjected to Cyclic Buckling or Cyclic End Moments and Buckling (User's Guide for Drain-2d:EL9 and EL10)

https://deepblue.lib.umich.edu/bitstream/2027.42/154132/1/39015094008078.pd

SoD-TEAM: Teleological reasoning in adaptive software design

Issued as final reportNational Science Foundation (U.S.

Evaluating Biological Systems for Their Potential in Engineering Design

A team of biologists, engineers, and cognitive scientists has been working together for the past five years, teaching an upper level undergraduate course in biologically inspired design where half the class of forty students are biologists and other physical scientists and the other half are engineers (mechanical, materials, industrial, others). From this experience, we provide insights on how to teach students to evaluate biological systems for their potential in engineering design. We have found that at first, students are not familiar with developing their own question since, in most engineering design classes, the problem is prescribed along with clients who would like to have them solved. In our class, we challenge the students with defining a significant problem. The students with common challenges then are placed together in an interdisciplinary team with at least one biologist and one engineer. A detailed problem decomposition follows, identifying the hierarchy of systems and clearly specifying functions. This is essential for the next step of analogical reasoning. Analogical reasoning as applied to BID is a process of matching biological functions to engineered functions and transferring functions and mechanisms from biology to engineering. For each desired function, students may ask: what mechanisms does nature use for achieving the function? This question guides the exploration of the wealth of knowledge in biology by asking them to clearly define the function of interest, then search for natural processes that perform this function. To expand on this search space, the students next make a list of the same function performed by other organisms for a comparative analysis to deepen their understanding and extract key biological principles. Students then invert the function and identify keywords to search. They also must refer to general biology books to identify key organisms that perform the function the best (and hence are included in textbooks). Using databases, such as the Web of Science functions, they can try to select the ‘best’ articles. If one is lucky, a single biological system may serve as a near perfect match to lead to a successful BID. However, some of the most innovative designs are built from more than one biological system, something that evolution cannot always do. We call these compound analogies. At this point, the design iteration can take on a different approach, namely solution based rather than problem based. Here, the team takes a natural system and asks, how can this biological principle improve an engineered design or function. These twin processes: solution vs problem-based approaches both have led to innovative and creative design concepts in this interdisciplinary class. Key words: Biological systems; engineering design; interdisciplinary clas

An Experiment in Teaching Cognitive Systems Online

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

Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator

It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator, which does not require rigorous modelling as required in conventional techniques. Proposed Artificial Neural Network (ANN) model has been implemented to predict the effect of speed, capacitance and load on generated voltage and frequency of SEIG. Experimental data is used for the training of ANN. Results obtained from the trained ANN are found to be in close agreement with the experimental results.Facultad de Informátic