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Making a Better Robot Brain

Traditionally computers have not been very good at making decisions when faced with incomplete information, risk, or incredible complexity. Instead we often rely on human intuition to inform these decisions when there is no clear answer and in some cases this can actually be an incredibly effective way to make decisions.

Often you will hear a story of a fire fighter knowing the way to go based on a gut feeling, a batter knowing when to swing, or even a chef knowing exactly what to add to a recipe. You may ask yourself “how do they do that?” In all three of these cases people are relying on heuristics to inform their decision. A heuristic is a basic rule of thumb that lets us take a large complex and seemingly impossible problem and substitute it for a much easier problem that we can answer. As humans we rely on many different heuristics to inform decisions we make and we are often not aware of them and just attribute it to “intuition”. As we learn more and gain experience we can hone our heuristics and become better decision makers when facing specific types of problems. The way we decide to consciously or subconsciously adjust our heuristics is called a meta-heuristic (applying a heuristic to our heuristics).

So what happens if we try to make a machine that is inspired by the way humans make their decisions when the problem is to big? Well we apply a heuristic algorithm of course! Heuristic algorithms are often applied to tricky problems to make them a little easier, but they traditionally can only be applied to certain types of problems leading a to a robot brain that can handle a big problem, but only big problems it has seen before. However, if we were to combine fast heuristic algorithms and calculations with slow strategic processes into a structure that selects the right type of approach for the problem, then a new better robot brain would emerge. This robot brain can handle uncertainty, risk, and complexity incredibly efficiently and while it won’t always make the best decision, it is capable of making really good decisions under the most challenging conditions. This approach is inspired by human cognition and the modeling of how our brains function which is called computational cognition. A robot brain that applies this approach is called a computationally cognitive agent or a comp cog agent for short.

In my dissertation titled “Autonomous Decision Making Facing Uncertainty, Risk, and Complexity” I explore this problem and then develop a comp cog agent capable of making incredible decisions under challenging circumstances. You can read my dissertation by clicking the link below.

Dr. Short’s CV

Professional Experience

Engineering Instructor, Oregon State University
April 2018-Present

Teaching ENGR 248 and ME 383, for both of these classes I developed new curriculum materials that focused on the different ways students learn.

Senior Mechatronic Design Engineer, Lora DiCarlo
April 2018-January 2020

Integrated design of consumer electronic products from concept through manufacture, managing junior engineering staff, writing SBIR proposals, and carrying out research and development of new products and technologies. I lead mechanical, electrical, and firmware design. Won three CES Design Innovation awards.

Mechanical Engineer, Sphero Robotics Inc/Misty Robotics
December 2014-February 2017

Mechanical design on several products including BB-8 (from Star Wars), SPRK+, Ultimate Lightning McQueen, and the home robot Misty. Additionally, I ran research and development of potential products and feasibility testing.

Analytics Intern, GRANT Engineering
Summer 2013

Development of analysis and decision-making tools for engineering project management.

Engineering and Project Management Intern, Smith Pump Company
Summer 2012

Development of software tools for design and manufacture of pump systems.

Education

PhD in Mechanical Engineering, Oregon State University
June 2018

Completed with a 4.0 GPA. My dissertation was on creation and analysis of autonomous decision makers that face risk, uncertainty, and complexity for control of robotic systems and automated design problems. Research partially funded by the National Science Foundation.

Master of Science in Mechanical Engineering, Colorado School of Mines
May 2016

Received CIE Outstanding Graduate Research Award for my work on risk informed decision making, published an ICED top paper, and was a Mechanical Engineering Department Honoree. Research funding came from the Nuclear Regulatory Commission and a NASA Early Stages Innovation Grant.

Bachelor of Science in Mechanical Engineering, Baylor University
May 2014

Deans Gold Scholarship Recipient, member of the Engineering Honors College, and Math Minor. Additionally, I performed biomechanics and biomaterials research with Dr. Carolyn Skurla including gait modeling and analysis.

Skills

  • Computational Cognition
  • Robotics and Mechatronics
  • Design Automation
  • Game Theory
  • Decision Theory
  • Machine Learning
  • Design Theory and Methods (DTM)
  • Autonomous Decision Making
  • Consumer Product Design
  • Project Management
  • Mathematical Modeling
  • Computer Aided Design
  • Programming
  • Additive Manufacturing

Publications

Autonomous Decision Making Facing Uncertainty, Risk, and Complexity
Doctoral Dissertation, Published June 2018

A Comparison of Tree Search Methods for Graph Topology Design Problems
International Conference on-Design Computing and Cognition, 2018

Conceptual Design of Sacrificial Sub-Systems: Failure Flow Decision Functions
Research In Engineering Design, 2018

Active Mission Success Estimation through Functional Modeling
Research In Engineering Design, 2018

Autonomous System Design and Controls Design for Operations in High Risk Environments
Proceedings of the ASME 2016 International Design Engineering Technical Conferences

Towards Risk-Informed Operation of Autonomous Vehicles to Increase Resilience in Unknown and Dangerous Environments
Proceedings of the ASME 2016 International Design Engineering Technical Conferences

Design of Autonomous Systems for Survivability through Conceptual Object-Based Risk Analysis
Masters Thesis, Published May 2016

PHM Informed Robotic Mission Control
Proceedings of the AIAA RMS Annual Technical Symposium 2015

Active Mission Success Estimation through PHM-Informed Probabilistic Modelling
Proceedings of the Annual Conference of the Prognostics and Health Management Society 2015

Risk Attitude Informed Route Planning in a Simulated Planetary Rover
Proceedings of the ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference August of 2015

Rerouting Failure Flows Using Logic Blocks in Functional Models for Improved System Robustness: Failure Flow Decision Functions 
Proceedings of the 2015 International Conference on Engineering Design, July of 2015

Patents

Multi-body self propelled device
WO2017165283 A2

Hybrid Additive Manufacturing Method and Apparatus Made Therefrom
US20170057164A1

Charging Unit for Mobile Devices
US20180191181A1

8 Currently Pending for Consumer Robotic Products
Information Available on Request