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Moshe Bitan

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5 papers

2 author rows

AAMAS Conference 2018 Conference Paper

Combining Prediction of Human Decisions with ISMCTS in Imperfect Information Games

Moshe Bitan
Sarit Kraus

We present agents that perform well against humans in imperfect information games with partially observable actions. We introduce the Semi-Determinized-MCTS (SDMCTS), a variant of the Information Set MCTS algorithm (ISMCTS). SDMCTS generates a predictive model of the unobservable portion of the opponent’s actions from historical behavioral data. Next, SDMCTS performs simulations on an instance of the game where the unobservable portion of the opponent’s actions are determined. Thereby, it facilitates the use of the predictive model in order to decrease uncertainty. We present an implementation of the SDMCTS applied to the Cheat Game. Results from experiments with 120 subjects playing a head-to-head Cheat Game against our SDMCTS agents suggest that SDMCTS performs well against humans, and its performance improves as the predictive model’s accuracy increases.

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IROS Conference 2018 Conference Paper

UAV/UGV Search and Capture of Goal-Oriented Uncertain Targets*This research was supported in part by ISF grant #1337/15 and part by a grant from MOST, Israel and the JST Japan

Mor Sinay
Noa Agmon
Oleg Maksimov
Guy Levy
Moshe Bitan
Sarit Kraus

This paper considers a new, complex problem of UAV/UGV collaborative efforts to search and capture attackers under uncertainty. The goal of the defenders (UAV/UGV team) is to stop all attackers as quickly as possible, before they arrive at their selected goal. The uncertainty considered is twofold: the defenders do not know the attackers' location and destination, and there is also uncertainty in the defenders' sensing. We suggest a real-time algorithmic framework for the defenders, combining entropy and stochastic-temporal belief, that aims at optimizing the probability of a quick and successful capture of all of the attackers. We have empirically evaluated the algorithmic framework, and have shown its efficiency and significant performance improvement compared to other solutions.

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AAAI Conference 2017 Conference Paper

Psychologically Based Virtual-Suspect for Interrogative Interview Training

Moshe Bitan
Galit Nahari
Zvi Nisin
Ariel Roth
Sarit Kraus

In this paper, we present a Virtual-Suspect system which can be used to train inexperienced law enforcement personnel in interrogation strategies. The system supports different scenario conﬁgurations based on historical data. The responses presented by the Virtual-Suspect are selected based on the psychological state of the suspect, which can be conﬁgured as well. Furthermore, each interrogator’s statement affects the Virtual-Suspect’s current psychological state, which may lead the interrogation in different directions. In addition, the model takes into account the context in which the statements are made. Experiments with 24 subjects demonstrate that the Virtual-Suspect’s behavior is similar to that of a human who plays the role of the suspect.

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IJCAI Conference 2013 Conference Paper

Predicting Human Strategic Decisions Using Facial Expressions

Noam Peled
Moshe Bitan
Joseph Keshet
Sarit Kraus

People’s facial expressions, whether made consciously or subconsciously, continuously reveal their state of mind. This work proposes a method for predicting people’s strategic decisions based on their facial expressions. We designed a new version of the centipede game that intorduces an incentive for the human participant to hide her facial expressions. We recorded on video participants who played several games of our centipede version, and concurrently logged their decisions throughout the games. The video snippet of the participants’ faces prior to their decisions is represented as a fixed-size vector by estimating the covariance matrix of key facial points which change over time. This vector serves as input to a classifier that is trained to predict the participant’s decision. We compare several training techniques, all of which are designed to work with the imbalanced decisions typically made by the players of the game. Furthermore, we investigate adaptation of the trained model to each player individually, while taking into account the player’s facial expressions in the previous games. The results show that our method outperforms standard SVM as well as humans in predicting subjects’ strategic decisions. To the best of our knowledge, this is the first study to present a methodology for predicting people’s strategic decisions when there is an incentive to hide facial expressions.

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AAAI Conference 2013 Conference Paper

Social Rankings in Human-Computer Committees

Moshe Bitan
Ya’akov Gal
Sarit Kraus
Elad Dokow
Amos Azaria

Despite committees and elections being widespread in the real-world, the design of agents for operating in humancomputer committees has received far less attention than the theoretical analysis of voting strategies. We address this gap by providing an agent design that outperforms other voters in groups comprising both people and computer agents. In our setting participants vote by simultaneously submitting a ranking over a set of candidates and the election system uses a social welfare rule to select a ranking that minimizes disagreements with participants’ votes. We ran an extensive study in which hundreds of people participated in repeated voting rounds with other people as well as computer agents that differed in how they employ strategic reasoning in their voting behavior. Our results show that over time, people learn to deviate from truthful voting strategies, and use heuristics to guide their play, such as repeating their vote from the previous round. We show that a computer agent using a best response voting strategy was able to outperform people in the game. Our study has implication for agent designers, highlighting the types of strategies that enable agents to succeed in committees comprising both human and computer participants. This is the first work to study the role of computer agents in voting settings involving both human and agent participants.

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