Approach

Participants controlled a UAV, flying over the Bagram Air Force Base, and were tasked with the goal of reaching a designated target. Success was incentivized with a $3 payout for each victory in this game.

Strategic State Estimation in Uncertain andMixed Multiagent Environments

Roi CerenComputer Science Department

The University of Georgiaceren@cs.uga.edu

Background

In the realm of decision making, uncertainty serves to obfuscate optimal or correct choices. Human judgment suffers from cognitive biases, which presents unique obstacles to efficient and meaningful probability estimations. This is especially apparent in complex environments, such as an UAV pilot in a multiagent environment.

The goal of these ARO studies is to identify potential loci of prediction errors and establish potential mechanisms for more effective field-valid assessment and update techniques. The project consists of three studies:

Results

Results were observed utilizing information contained within the experimental spreadsheet. The ratio between believed and stated odds (the X parameter) was analyzed. The closer X is to 1, the closer the believed probability assessment is to the stated probability assessment.

Population mean of X for RP: 0.07178 95% confidence interval: (0.000007457, 690.97)

Population mean of X for ROTC: 1.19195% confidence interval: (0.8542, 1.6608)

Both of these intervals contain 1 for X, indicating that the players may not have been significantly inflating or deflating their verbal estimations. Additionally, the tighter bound on the ROTC participants indicate a more consistent, better behaved research pool.

ContributionsThese experiment teaches us about the limits of human probability estimations and potential mechanisms that can assist in promoting better estimation. This study offers ways to quantify and analyze an individual’s cognitive biases as well as establish better assessment and update techniques.

References1. J. E. Mazur. Estimation of indifference points with an adjusting-delay procedure. Journal ofthe Experimental Analysis of Behavior, 49:37–47, 1988.2. B. DeFinetti. Foresight: Its logical laws, its subjective sources. In H. Kyburg and H. Smokler,editors, Studies in Subjective Probability, pages 93–158. New York City, New York, 1964.3. D. Kahneman, P. Slovic, and A. T. (Eds.). Judgment under Uncertainty: Heuristic and Biases.Cambridge University Press, 1982.4. D. Kahneman and A. Tversky. On the psychology of prediction. Psychological Review,80:237–251, 1973.

AcknowledgmentsThis work was performed in collaboration with Profs. Prashant Doshi (CS), Adam Goodie (Psychology) and Dan Hall (Statistics). We acknowledge the support of a grant from the Army RDECOM, grant # W911NF-09-1-0464, to Prof. Prashant Doshi (PI). Special thanks to Ekhlas Sonu for his work on GaTAC, the FlightGear community for quick, timely assistance, and Matthew Meisel for his help in running and maintaining the experiment.

The experiments were conducted through the University of Georgia Psychology department using the student research pool (RP) and Air Force ROTC. Participants are introduced to a flight simulator (FlightGear) with a custom environment developed by our lab, known as GaTAC (Georgia Testbed for Autonomous Control of Vehicles).

Participants experienced two phases of experimentation: training and test phases. In the training phase, participants were given the opportunity to grow accustomed to the interface. In the test phase, participants experienced 15 trials. During each decision point, players filled out a questionnaire quantifying their probability assessments.

For each decision point, if the player chose to make a move without the random event, the probability of winning the random event increases. Inversely, choosing the random event decreases the likelihood of winning the random event. [1] Ideally, the probability estimates for experimental player will converge on a validated estimate.

The experimental group was given the opportunity at each decision point to validate their probability by using a random number generator (called a bingo cage) as an opportunity to make a move with no risk, based on their estimate.

The current wave of research (Study 1) attempts to highlight disparities between a verbal expressions of probabilities and the actual belief of a given individual.

Introduction

Humans, in generating probability estimations for their predictions, often lack justification. In realistic situations, such as military operations, judging uncertainty can be difficult. As current research does not apply to complex situations, we designed three studies in order to establish and encourage field-valid probability assessments with effective update mechanisms. Utilizing UAV simulation, we were able to present a complex, real environment to subjects. Each study seeks to identify causal sources of poor probability assessments and seeks to rectify the associated cause. Our most recent progress on this research involves the completion of the first study, where we investigated the impact of subjective expressions of probabilities and potential methods to correct them.