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Inon Zuckerman

Ariel University, Industrial Engineering and Management, Faculty Member

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I'm a faculty member in the Department of Industrial Engineering and Management at Ariel University.I am researching in the field of Artificial Intelligence on problems relating to the development of autonomous agents, human-agent interaction, game playing, cognitive modeling, and neuroIS.My work is very interdisciplinary in nature and intersects with the social and behavioral sciences as well as the brain sciences.

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Papers by Inon Zuckerman

Guiding User Choice During Discussion by Silence, Examples and Justifications

This paper describes an approach for guiding human choice-making by a computerized agent, in a co... more This paper describes an approach for guiding human choice-making by a computerized agent, in a conversational setting, where both user and agent provide meaningful input. In the proposed approach, the agent attempts to convince a person by providing examples for the person to emulate or by providing justifications for the person to internalize and build or change her preferences accordingly. The agent can take into account examples and justifications provided by the person. In a series of experiments where the task was selecting a location for a school, a computer agent interacted with subjects using a textual chat-type interface, with different agent designs being used in different experiments. The results show that the example-providing agent outperformed the justification providing agent and both, surprisingly, outperformed an agent which presented the subject with both examples and justifications. In addition, it was demonstrated that in some cases the best strategy for the agent is to keep silent. ECAI 2012 Luc De Raedt et al. (Eds.)

Agent Models for Enhanced Interactions in Cooperative and Competitive Environments - Research outline

A BDI-based agent architecture for social competent agents

Web Intelligence, 2015

Agent Models for Enhanced Interactions in Cooperative and Competitive Environments - Research outline

First Steps in Chat-Based Negotiating Agents

Studies in Computational Intelligence, 2015

NegoChat-A: a chat-based negotiation agent with bounded rationality

by Inon Zuckerman and אסנת דריין

Autonomous Agents and Multi-Agent Systems, 2015

To date, a variety of automated negotiation agents have been created. While each of these agents ... more To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they typically lack the natural language processing support required to enable realworld types of interactions. To address this limitation, we present NegoChat-A, an agent that incorporates several significant research contributions. First, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead, agents that support natural language must have strategies that allow for partial agreements and issue-by-issue interactions. Second, we present NegoChat-A's negotiation algorithm. This algorithm is based on bounded rationality, and specifically anchoring and Aspiration Adaptation Theory (AAT). The agent begins each negotiation interaction by proposing a full offer, which serves as its anchor. Assuming this offer is not accepted, the agent then proceeds to negotiate via partial agreements, proposing the next issue for negotiation based on people's typical urgency, or order of importance. We present a rigorous evaluation of NegoChat-A, showing its effectiveness in two different negotiation roles.

Reasoning about Groups: A Cognitive Model for the Social Behavior Spectrum

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011

An important aspect of Social Intelligence is the ability to correctly capture the social structu... more An important aspect of Social Intelligence is the ability to correctly capture the social structure and use it to navigate and achieve ones goals. In this work we suggest a mental model that provides agents with similar social capabilities. The model captures the entire social behavior spectrum, and provides design principles that allow agents to reason and change their behavior according to their perception of the cooperative/competitive nature of the society. We also describe computationally the maximum attainable benefits when agents belong to different kinds of social groups. We conclude by exploring the group membership problem as a constraints satisfaction problem, and evaluate few heuristics.

Guiding user choice during discussion by silence, examples and justifications

by Inon Zuckerman and Maier Fenster

This paper describes an approach for guiding human choice-making by a computerized agent, in a co... more This paper describes an approach for guiding human choice-making by a computerized agent, in a conversational setting, where both user and agent provide meaningful input. In the proposed approach, the agent attempts to convince a person by providing examples for the person to emulate or by providing justifications for the person to internalize and build or change her preferences accordingly. The agent can take into account examples and justifications provided by the person. In a series of experiments where the task was selecting a location for a school, a computer agent interacted with subjects using a textual chat-type interface, with different agent designs being used in different experiments. The results show that the example-providing agent outperformed the justification providing agent and both, surprisingly, outperformed an agent which presented the subject with both examples and justifications. In addition, it was demonstrated that in some cases the best strategy for the agent is to keep silent. ECAI 2012 Luc De Raedt et al. (Eds.)

Negochat: A chat-based negotiation agent

To date, a variety of automated negotiation agents have been created. While each of these agents ... more To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first negotiation agent that successfully addresses this limitation. NegoChat contains several significant research contributions. First, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead, the agents' strategies must be modified to address partial agreements and issue-byissue interactions. Second, we present NegoChat's negotiation algorithm. This algorithm is based on bounded rationality, and specifically Aspiration Adaptation Theory (AAT). As per AAT, issues are addressed based on people's typical urgency, or order of importance. If an agreement cannot be reached based on the value the human partner demands, the agent retreats, or downwardly lowers the value of previously agreed upon issues so that a "good enough" agreement can be reached on all issues. This incremental approach is fundamentally different from all other negotiation agents, including the state-of-the-art KBAgent. Finally, we present a rigorous evaluation of NegoChat, showing its effectiveness.

Predicting Agents’ Behavior by Measuring their Social Preferences

by Kan-Leung Cheng and Inon Zuckerman

There are many situations in which two or more agents (e.g., human or computer decision makers) i... more There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated stochastic games. In such situations, each agent's performance may depend greatly on how well it can predict the other agents' preferences and behavior. For use in making such predictions, we adapt and extend the Social Value Orientation (SVO) model from social psychology, which provides a way to measure an agent's preferences for both its own payoffs and those of the other agents.

Improving Local Decisions in Adversarial Search

Until recently, game-tree pathology (in which a deeper game-tree search results in worse play) ha... more

The social landscape: reasoning on the social behavior spectrum

IEEE Intelligent Systems, 2000

Proliferating social integration between humans and computational entities increases the need for... more

Modeling Social Preferences in Multi-player Games

Proc. of AAMAS'11, 2011

Game-tree search algorithms have contributed greatly to the success of computerized players in tw... more

Manipulating boolean games through communication

by John Grant and Inon Zuckerman

We address the issue of manipulating games through communication. In the specific setting we cons... more We address the issue of manipulating games through communication. In the specific setting we consider (a variation of Boolean games), we assume there is some set of environment variables, the value of which is not directly accessible to players; each player has their own beliefs about these variables, and makes decisions about what actions to perform based on these beliefs. The communication we consider takes the form of (truthful) announcements about the value of some environment variables; the effect of an announcement about some variable is to modify the beliefs of the players who hear the announcement so that they accurately reflect the value of the announced variables. By choosing announcements appropriately, it is possible to perturb the game away from certain rational outcomes and towards others. We specifically focus on the issue of stabilisation: making announcements that transform a game from having no stable states to one that has stable configurations.

Combining psychological models with machine learning to better predict people’s decisions

by Amos Azaria and Inon Zuckerman

Using Focal Point Learning to Improve Tactic Coordination in Human-Machine Interactions

by Inon Zuckerman and Jeffrey S Rosenschein

International Joint Conference on Artificial Intelligence, 2007

We consider an automated agent that needs to coor- dinate with a human partner when communication... more We consider an automated agent that needs to coor- dinate with a human partner when communication between them is not possible or is undesirable (tac- tic coordination games). Specifically, we exam- ine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human

Using focal point learning to improve human-machine tacit coordination

by Inon Zuckerman and Jeffrey S Rosenschein

Autonomous Agents and Multi-Agent Systems

We consider an automated agent that needs to coordinate with a human partner when communication b... more We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tacit coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tacit coordination domains. Experiments have shown that humans are often able to coordinate with one another in communication-free games, by using focal points, “prominent” solutions to coordination problems. We integrate focal point rules into the machine learning process, by transforming raw domain data into a new hypothesis space. We present extensive empirical results from three different tacit coordination domains. The Focal Point Learning approach results in classifiers with a 40–80% higher correct classification rate, and shorter training time, than when using regular classifie...

An adversarial environment model for bounded rational agents in zero-sum interactions

by Inon Zuckerman and Jeffrey S Rosenschein

Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems - AAMAS '07, 2007

Multiagent environments are often not cooperative nor collaborative; in many cases, agents have c... more Multiagent environments are often not cooperative nor collaborative; in many cases, agents have conflicting interests, leading to adversarial interactions. This paper presents a formal Adversarial Environment model for bounded rational agents operating in a zero-sum environment. In such environments, attempts to use classical utility-based search methods can raise a variety of difficulties (e.g., implicitly modeling the opponent as an omniscient utility maximizer, rather than leveraging a more nuanced, explicit opponent model).

The Life Game: Cognitive Strategies for Repeated Stochastic Games

by Inon Zuckerman and Kan-leung Cheng

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011

Standard models in bio-evolutionary game theory involve repetitions of a single stage game (e.g.,... more Standard models in bio-evolutionary game theory involve repetitions of a single stage game (e.g., the Prisoner's Dilemma or the Stag Hunt); but it is clear that repeatedly playing the same stage game is not an accurate model of most individuals' lives. Rather, individuals' interactions with others correspond to many different kinds of stage games.

Mixing Search Strategies for MultiPlayer Games

by Inon Zuckerman and Ariel Felner

International Joint Conference on Artificial Intelligence, 2009

There are two basic approaches to generalize the propa- gation mechanism of the two-player Minima... more There are two basic approaches to generalize the propa- gation mechanism of the two-player Minimax search al- gorithm to multi-player (3 or more) games: the MaxN algorithm and the Paranoid algorithm. The main short- coming of these approaches is that their strategy is fixed. In this paper we suggest a new approach (called MP- Mix) that dynamically changes the propagation

Guiding User Choice During Discussion by Silence, Examples and Justifications

This paper describes an approach for guiding human choice-making by a computerized agent, in a co... more This paper describes an approach for guiding human choice-making by a computerized agent, in a conversational setting, where both user and agent provide meaningful input. In the proposed approach, the agent attempts to convince a person by providing examples for the person to emulate or by providing justifications for the person to internalize and build or change her preferences accordingly. The agent can take into account examples and justifications provided by the person. In a series of experiments where the task was selecting a location for a school, a computer agent interacted with subjects using a textual chat-type interface, with different agent designs being used in different experiments. The results show that the example-providing agent outperformed the justification providing agent and both, surprisingly, outperformed an agent which presented the subject with both examples and justifications. In addition, it was demonstrated that in some cases the best strategy for the agent is to keep silent. ECAI 2012 Luc De Raedt et al. (Eds.)

Agent Models for Enhanced Interactions in Cooperative and Competitive Environments - Research outline

A BDI-based agent architecture for social competent agents

Web Intelligence, 2015

Agent Models for Enhanced Interactions in Cooperative and Competitive Environments - Research outline

First Steps in Chat-Based Negotiating Agents

Studies in Computational Intelligence, 2015

NegoChat-A: a chat-based negotiation agent with bounded rationality

by Inon Zuckerman and אסנת דריין

Autonomous Agents and Multi-Agent Systems, 2015

To date, a variety of automated negotiation agents have been created. While each of these agents ... more To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they typically lack the natural language processing support required to enable realworld types of interactions. To address this limitation, we present NegoChat-A, an agent that incorporates several significant research contributions. First, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead, agents that support natural language must have strategies that allow for partial agreements and issue-by-issue interactions. Second, we present NegoChat-A's negotiation algorithm. This algorithm is based on bounded rationality, and specifically anchoring and Aspiration Adaptation Theory (AAT). The agent begins each negotiation interaction by proposing a full offer, which serves as its anchor. Assuming this offer is not accepted, the agent then proceeds to negotiate via partial agreements, proposing the next issue for negotiation based on people's typical urgency, or order of importance. We present a rigorous evaluation of NegoChat-A, showing its effectiveness in two different negotiation roles.

Reasoning about Groups: A Cognitive Model for the Social Behavior Spectrum

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011

An important aspect of Social Intelligence is the ability to correctly capture the social structu... more An important aspect of Social Intelligence is the ability to correctly capture the social structure and use it to navigate and achieve ones goals. In this work we suggest a mental model that provides agents with similar social capabilities. The model captures the entire social behavior spectrum, and provides design principles that allow agents to reason and change their behavior according to their perception of the cooperative/competitive nature of the society. We also describe computationally the maximum attainable benefits when agents belong to different kinds of social groups. We conclude by exploring the group membership problem as a constraints satisfaction problem, and evaluate few heuristics.

Guiding user choice during discussion by silence, examples and justifications

by Inon Zuckerman and Maier Fenster

This paper describes an approach for guiding human choice-making by a computerized agent, in a co... more This paper describes an approach for guiding human choice-making by a computerized agent, in a conversational setting, where both user and agent provide meaningful input. In the proposed approach, the agent attempts to convince a person by providing examples for the person to emulate or by providing justifications for the person to internalize and build or change her preferences accordingly. The agent can take into account examples and justifications provided by the person. In a series of experiments where the task was selecting a location for a school, a computer agent interacted with subjects using a textual chat-type interface, with different agent designs being used in different experiments. The results show that the example-providing agent outperformed the justification providing agent and both, surprisingly, outperformed an agent which presented the subject with both examples and justifications. In addition, it was demonstrated that in some cases the best strategy for the agent is to keep silent. ECAI 2012 Luc De Raedt et al. (Eds.)

Negochat: A chat-based negotiation agent

To date, a variety of automated negotiation agents have been created. While each of these agents ... more To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first negotiation agent that successfully addresses this limitation. NegoChat contains several significant research contributions. First, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead, the agents' strategies must be modified to address partial agreements and issue-byissue interactions. Second, we present NegoChat's negotiation algorithm. This algorithm is based on bounded rationality, and specifically Aspiration Adaptation Theory (AAT). As per AAT, issues are addressed based on people's typical urgency, or order of importance. If an agreement cannot be reached based on the value the human partner demands, the agent retreats, or downwardly lowers the value of previously agreed upon issues so that a "good enough" agreement can be reached on all issues. This incremental approach is fundamentally different from all other negotiation agents, including the state-of-the-art KBAgent. Finally, we present a rigorous evaluation of NegoChat, showing its effectiveness.

Predicting Agents’ Behavior by Measuring their Social Preferences

by Kan-Leung Cheng and Inon Zuckerman

There are many situations in which two or more agents (e.g., human or computer decision makers) i... more There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated stochastic games. In such situations, each agent's performance may depend greatly on how well it can predict the other agents' preferences and behavior. For use in making such predictions, we adapt and extend the Social Value Orientation (SVO) model from social psychology, which provides a way to measure an agent's preferences for both its own payoffs and those of the other agents.

Improving Local Decisions in Adversarial Search

Until recently, game-tree pathology (in which a deeper game-tree search results in worse play) ha... more

The social landscape: reasoning on the social behavior spectrum

IEEE Intelligent Systems, 2000

Proliferating social integration between humans and computational entities increases the need for... more

Modeling Social Preferences in Multi-player Games

Proc. of AAMAS'11, 2011

Game-tree search algorithms have contributed greatly to the success of computerized players in tw... more

Manipulating boolean games through communication

by John Grant and Inon Zuckerman

We address the issue of manipulating games through communication. In the specific setting we cons... more We address the issue of manipulating games through communication. In the specific setting we consider (a variation of Boolean games), we assume there is some set of environment variables, the value of which is not directly accessible to players; each player has their own beliefs about these variables, and makes decisions about what actions to perform based on these beliefs. The communication we consider takes the form of (truthful) announcements about the value of some environment variables; the effect of an announcement about some variable is to modify the beliefs of the players who hear the announcement so that they accurately reflect the value of the announced variables. By choosing announcements appropriately, it is possible to perturb the game away from certain rational outcomes and towards others. We specifically focus on the issue of stabilisation: making announcements that transform a game from having no stable states to one that has stable configurations.

Combining psychological models with machine learning to better predict people’s decisions

by Amos Azaria and Inon Zuckerman

Using Focal Point Learning to Improve Tactic Coordination in Human-Machine Interactions

by Inon Zuckerman and Jeffrey S Rosenschein

International Joint Conference on Artificial Intelligence, 2007

We consider an automated agent that needs to coor- dinate with a human partner when communication... more We consider an automated agent that needs to coor- dinate with a human partner when communication between them is not possible or is undesirable (tac- tic coordination games). Specifically, we exam- ine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human

Using focal point learning to improve human-machine tacit coordination

by Inon Zuckerman and Jeffrey S Rosenschein

Autonomous Agents and Multi-Agent Systems

We consider an automated agent that needs to coordinate with a human partner when communication b... more We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tacit coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tacit coordination domains. Experiments have shown that humans are often able to coordinate with one another in communication-free games, by using focal points, “prominent” solutions to coordination problems. We integrate focal point rules into the machine learning process, by transforming raw domain data into a new hypothesis space. We present extensive empirical results from three different tacit coordination domains. The Focal Point Learning approach results in classifiers with a 40–80% higher correct classification rate, and shorter training time, than when using regular classifie...

An adversarial environment model for bounded rational agents in zero-sum interactions

by Inon Zuckerman and Jeffrey S Rosenschein

Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems - AAMAS '07, 2007

Multiagent environments are often not cooperative nor collaborative; in many cases, agents have c... more Multiagent environments are often not cooperative nor collaborative; in many cases, agents have conflicting interests, leading to adversarial interactions. This paper presents a formal Adversarial Environment model for bounded rational agents operating in a zero-sum environment. In such environments, attempts to use classical utility-based search methods can raise a variety of difficulties (e.g., implicitly modeling the opponent as an omniscient utility maximizer, rather than leveraging a more nuanced, explicit opponent model).

The Life Game: Cognitive Strategies for Repeated Stochastic Games

by Inon Zuckerman and Kan-leung Cheng

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011

Standard models in bio-evolutionary game theory involve repetitions of a single stage game (e.g.,... more Standard models in bio-evolutionary game theory involve repetitions of a single stage game (e.g., the Prisoner's Dilemma or the Stag Hunt); but it is clear that repeatedly playing the same stage game is not an accurate model of most individuals' lives. Rather, individuals' interactions with others correspond to many different kinds of stage games.

Mixing Search Strategies for MultiPlayer Games

by Inon Zuckerman and Ariel Felner

International Joint Conference on Artificial Intelligence, 2009

There are two basic approaches to generalize the propa- gation mechanism of the two-player Minima... more There are two basic approaches to generalize the propa- gation mechanism of the two-player Minimax search al- gorithm to multi-player (3 or more) games: the MaxN algorithm and the Paranoid algorithm. The main short- coming of these approaches is that their strategy is fixed. In this paper we suggest a new approach (called MP- Mix) that dynamically changes the propagation