Eng 101 – Seeds of Success Social and Ethical Implications of Artificial Intelligence Daniel Tauritz, Ph.D. Associate Professor of Computer Science

Algorithm An algorithm is a sequence of well-defined instructions that can be executed in a finite amount of time in order to solve some problem.

Optimization Algorithm An optimization algorithm is an algorithm which takes as input a solution space, an objective function which maps each point in the solution space to a linearly ordered set, and a desired goal element in the set.

Stochastic Algorithm A stochastic algorithm is an algorithm which when executed multiple times with the same input, produces different outputs drawn from some underlying probability distribution.

Evolutionary Algorithm A stochastic optimization algorithm inspired by genetics and natural evolution theory.

Deriving Gas-Phase Exposure History through Computationally Evolved Inverse Diffusion Analysis Joshua M. Eads Undergraduate in Computer Science Daniel Tauritz Associate Professor of Computer Science Glenn Morrison Associate Professor of Environmental Engineering Ekaterina Smorodkina Former Ph.D. Student in Computer Science

Introduction Unexplained Sickness Examine Indoor Exposure History Find Contaminants and Fix Issues

Background Indoor air pollution top five environmental health risks $160 billion could be saved every year by improving indoor air quality Current exposure history is inadequate A reliable method is needed to determine past contamination levels and times

Problem Statement A forward diffusion differential equation predicts concentration in materials after exposure An inverse diffusion equation finds the timing and intensity of previous gas contamination Knowledge of early exposures would greatly strengthen epidemiological conclusions

Gas-phase concentration history and material absorption

Proposed Solution x^2 + sin(x) sin(x+y) + e^(x^2) 5x^2 + 12x - 4 x^5 + x^4 - tan(y) / pi sin(cos(x+y)^2) x^2 - sin(x) X+ / Sin ? Use Genetic Programming (GP) as a directed search for inverse equation Fitness based on forward equation

Related Research It has been proven that the inverse equation exists Symbolic regression with GP has successfully found both differential equations and inverse functions Similar inverse problems in thermodynamics and geothermal research have been solved

Candidate Solutions Population Fitness Interdisciplinary Work Collaboration between Environmental Engineering, Computer Science, and Math Parent Selection ReproductionReproduction CompetitionCompetition Genetic Programming Algorithm Forward Diffusion Equation

Genetic Programming Background + * X Si n *X XPi Y = X^2 + Sin( X * Pi )

Summary Ability to characterize exposure history will enhance ability to assess health risks of chemical exposure

Social and Ethical Impacts Tool to improve health

A Coevolutionary Arms-Race Methodology for Improving Electric Power Transmission System Reliability

Delivery Problems Transmission Grid Expansion Hampered –Social, environmental, and economic constraints Transmission Grid Already “Stressed” –Already carrying more than intended –Dramatic increase in incidence reports

The Grid

The Grid: Failure

The Grid: Redistribution

The Grid: A Cascade

The Grid: Redistribution

The Grid: Unsatisfiable

Failure Summary Failure spreads relatively quickly –Too quickly for conventional control Cascade may be avoidable –Utilize unused capacities (Flow compensation) Unsatisfiable condition may be avoidable –Better power flow control to reduce severity

Measuring hardening performance In practice: evaluate over a representative sampling of scenarios Sampling approaches –Pruned Exhaustive (e.g., n-1 security index in power systems) –Monte Carlo –Intelligent adversary

Intelligent Adversary Game Theoretic: Two-player game of defenders & attackers Dependent search spaces: grid hardening space (defenders) & scenario space (attackers) Computational methods for dependent search: –Iterative approach –Competitive Coevolution approach –Generalized Co-Optimization approach

Competitive Coevolution Type of Evolutionary Algorithm where solution quality is dependent on other solutions For two-player games an arms-race is created by having two opposing populations of solutions where solution quality is inversely dependent on solutions in the opposing population

Co-Optimization Generalization of Coevolution Evolutionary principles are replaced by arbitrary black-box optimization techniques Allows matching of interactive problem domains to optimization techniques

Summary of methodology Improve grid robustness by creating an arms-race between hardenings (defenders) and fault scenarios (attackers) through the use of Co-Optimization Hardenings are evolved to minimize economic loss Fault scenarios are evolved to maximize economic loss Stair stepping of ability

Advanced Power Transmission System with Distributed Power Electronics Devices - Case Study Hardenings: Unified Power Flow Controller (UPFC) placements –Control power flow through transmission lines –UPFCs are a powerful type of Flexible AC Transmission System (FACTS) device Fault scenarios: line outages

FACTS Interaction Laboratory HIL Line UPFC Simulation Engine

Social & Ethical Implications Grid really improved? –Margins versus profits –New weaknesses introduced Who decides delivery priorities? Can this research be misused?

Coevolutionary Automated Software Correction (CASC) ISC Sponsored Project Ph.D. student: Josh Wilkerson

Objective: Find a way to automate the process of software testing and correction. Approach: Create Coevolutionary Automated Software Correction (CASC) system which will take a software artifact as input and produce a corrected version of the software artifact as output.

Coevolutionary Cycle

Population Initialization

Initial Evaluation

Reproduction Phase

Evaluation Phase

Competition Phase

Termination

Social and Ethical Impacts Will this lead to higher unemployment?