Noam Peled

YNIMG Journal 2022 Journal Article

Brain structure and episodic learning rate in cognitively healthy ageing

• Darya Frank
• Marta Garo-Pascual
• Pablo Alejandro Reyes Velasquez
• Belén Frades
• Noam Peled
• Linda Zhang
• Bryan A. Strange

YNIMG Journal 2021 Journal Article

Spatial fidelity of MEG/EEG source estimates: A general evaluation approach

• John G. Samuelsson
• Noam Peled
• Fahimeh Mamashli
• Jyrki Ahveninen
• Matti S. Hämäläinen

JAAMAS Journal 2014 Journal Article

A study of computational and human strategies in revelation games

• Noam Peled
• Ya’akov (Kobi) Gal
• Sarit Kraus

Abstract Many negotiations in the real world are characterized by incomplete information, and participants’ success depends on their ability to reveal information in a way that facilitates agreements without compromising their individual gain. This paper presents an agent-design that is able to negotiate proficiently with people in settings in which agents can choose to truthfully reveal their private information before engaging in multiple rounds of negotiation. Such settings are analogous to real-world situations in which people need to decide whether to disclose information such as when negotiating over health plans and business transactions. The agent combined a decision-theoretic approach with traditional machine-learning techniques to reason about the social factors that affect the players’ revelation decisions on people’s negotiation behavior. It was shown to outperform people as well as agents playing the equilibrium strategy of the game in empirical studies spanning hundreds of subjects. It was also more likely to reach agreement than people or agents playing equilibrium strategies. In addition, it had a positive effect on people’s play, allowing them to reach significantly better performance when compared to people’s play with other people. These results are shown to generalize for two different settings that varied how players depend on each other in the negotiation.

AAAI Conference 2013 Conference Paper

An Agent Design for Repeated Negotiation and Information Revelation with People

• Noam Peled
• Ya'akov Gal
• Sarit Kraus

Many negotiations in the real world are characterized by incomplete information, and participants’ success depends on their ability to reveal information in a way that facilitates agreement without compromising the individual gains of agents. This paper presents a novel agent design for repeated negotiation in incomplete information settings that learns to reveal information strategically during the negotiation process. The agent used classical machine learning techniques to predict how people make and respond to offers during the negotiation, how they reveal information and their response to potential revelation actions by the agent. The agent was evaluated empirically in an extensive empirical study spanning hundreds of human subjects. Results show that the agent was able to outperform people. In particular, it learned (1) to make offers that were beneficial to people while not compromising its own benefit; (2) to incrementally reveal information to people in a way that increased its expected performance. The approach generalizes to new settings without the need to acquire additional data. This work demonstrates the efficacy of combining machine learning with opponent modeling techniques towards the design of computer agents for negotiating with people in settings of incomplete information.

AAMAS Conference 2013 Conference Paper

An Agent Design for Repeated Negotiation and Information Revelation with People

• Noam Peled
• Ya'akov Kobi Gal
• Sarit Kraus

Many negotiations in the real world are characterized by incomplete information, and participants’ success depends on their ability to reveal information in a way that facilitates agreement without compromising the individual gains of agents. This paper presents a novel agent design for repeated negotiation in incomplete information settings that learns to reveal information strategically during the negotiation process. The agent used classical machine learning techniques to predict how people make and respond to offers during the negotiation, how they reveal information and their response to potential revelation actions by the agent. The agent was evaluated empirically in an extensive empirical study spanning hundreds of human subjects. Results show that the agent was able (1) to make offers that were beneficial to people while not compromising its own benefit; (2) to incrementally reveal information to people in a way that increased its expected performance. The agent also had a positive effect on people’s strategy, in that people playing the agent performed significantly higher than people playing other people. This work demonstrates the efficacy of combining machine learning with opponent modeling techniques towards the design of computer agents for negotiating with people in settings of incomplete information.

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.

AAMAS Conference 2011 Conference Paper

A Study of Computational and Human Strategies in Revelation Games

• Noam Peled
• Ya'akov (Kobi) Gal
• Sarit Kraus

Revelation games are bilateral bargaining games in which agents may choose to truthfully reveal their private information before engaging in multiple rounds of negotiation. They are analogous to real-world situations in which people need to decide whether to disclose information such as medical records or university transcripts when negotiating over health plans and business transactions. This paper presents an agent-design that is able to negotiate proficiently with people in a revelation game with different dependencies that hold between players. The agent modeled the social factors that affect the players' revelation decisions on people's negotiation behavior. It was empirically shown to outperform people in empirical evaluations as well as agents playing equilibrium strategies. It was also more likely to reach agreement than people or equilibrium agents.