Inon Zuckerman

JAAMAS Journal 2015 Journal Article

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

• Avi Rosenfeld
• Inon Zuckerman
• Sarit Kraus

Abstract 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 real-world 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 natural language processing 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. 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.

ECAI Conference 2014 Conference Paper

Predicting Agents' Behavior by Measuring their Social Preferences

• Kan-Leung Cheng
• Inon Zuckerman
• Dana S. Nau
• Jennifer Golbeck

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.

ECAI Conference 2012 Conference Paper

Guiding User Choice During Discussion by Silence, Examples and Justifications

• Maier Fenster
• Inon Zuckerman
• Sarit Kraus

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 pre sented 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 Conference 2012 Conference Paper

Improving Local Decisions in Adversarial Search

• Brandon Wilson
• Inon Zuckerman
• Austin Parker
• Dana S. Nau

Until recently, game-tree pathology (in which a deeper game-tree search results in worse play) has been thought to be quite rare. We provide an analysis that shows that every game should have some sections that are locally pathological, assuming that both players can potentially win the game.

IJCAI Conference 2011 Conference Paper

Manipulating Boolean Games through Communication

• John Grant
• Sarit Kraus
• Michael Wooldridge
• Inon Zuckerman

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.

AAMAS Conference 2011 Conference Paper

Modeling Social Preferences in Multi-player Games

• Brandon Wilson
• Inon Zuckerman
• Dana Nau

Game-tree search algorithms have contributed greatly to the success of computerized players in two-player extensive-form games. In multi-player games there has been less success, partly because of the difficulty of recognizing and reasoning about the inter-player relationships that often develop and change during human game-play. Simplifying assumptions (e. g. , assuming each player selfishly aims to maximize its own payoff) have not worked very well in practice. We describe a new algorithm for multi-player games, Socially-oriented Search (SOS), that incorporates ideas from Social Value Orientation theory from social psychology. We provide a theoretical study of the algorithm, and a method for recognizing and reasoning about relationships as they develop and change during a game. Our empirical evaluations of SOS in the strategic board game Quoridor show it to be significantly more effective against players with dynamic interrelationships than the current state-of-the-art algorithms.

JAAMAS Journal 2010 Journal Article

The adversarial activity model for bounded rational agents

• Inon Zuckerman
• Sarit Kraus
• Jeffrey S. Rosenschein

Abstract Multiagent research provides an extensive literature on formal Beliefs-Desires-Intentions (BDI) based models describing the notion of teamwork and cooperation. However, multiagent environments are often not cooperative nor collaborative; in many cases, agents have conflicting interests, leading to adversarial interactions. This form of interaction has not yet been formally defined in terms of the agents mental states, beliefs, desires and intentions. This paper presents the Adversarial Activity model, a formal Beliefs-Desires-Intentions (BDI) based model for bounded rational agents operating in a zero-sum environment. In complex environments, attempts to use classical utility-based search methods with bounded rational agents 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). We define the Adversarial Activity by describing the mental states of an agent situated in such environment. We then present behavioral axioms that are intended to serve as design principles for building such adversarial agents. We illustrate the advantages of using the model as an architectural guideline by building agents for two adversarial environments: the Connect Four game and the Risk strategic board game. In addition, we explore the application of our approach by analyzing log files of completed Connect Four games, and gain additional insights on the axioms’ appropriateness.

JAAMAS Journal 2010 Journal Article

Using focal point learning to improve human–machine tacit coordination

• Inon Zuckerman
• Sarit Kraus
• Jeffrey S. Rosenschein

Abstract 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 classifiers, and a 35% higher correct classification rate than classical focal point techniques without learning. In addition, the integration of focal points into learning algorithms results in agents that are more robust to changes in the environment. We also present several results describing various biases that might arise in Focal Point based coordination.

IJCAI Conference 2009 Conference Paper

• Inon Zuckerman
• Ariel Felner
• Sarit Kraus

There are two basic approaches to generalize the propagation mechanism of the two-player Minimax search algorithm to multi-player (3 or more) games: the MaxN algorithm and the Paranoid algorithm. The main shortcoming 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 strategy based on the players’ relative strengths between MaxN, Paranoid and a newly presented offensive strategy. In addition, we introduce the Opponent Impact factor for multi-player games, which measures the players’ ability to impact their opponents’ score, and show its relation to the relative performance of our new MP-Mix strategy. Experimental results show that MP-Mix outperforms all other approaches under most circumstances.

ECAI Conference 2008 Conference Paper

An Empirical Investigation of the Adversarial Activity Model

• Inon Zuckerman
• Sarit Kraus
• Jeffrey S. Rosenschein

Multiagent research provides an extensive literature on formal Belief-Desire-Intention (BDI) based models describing the notions of teamwork and cooperation, but adversarial and competitive relationships have received very little formal BDI treatment. Moreover, one of the main roles of such models is to serve as design guide-lines for the creation of agents, and while there is work illustrating that role in cooperative interaction, there has been no empirical work done to validate competitive BDI models.

IJCAI Conference 2007 Conference Paper

• Inon Zuckerman
• Sarit Kraus
• Jeffrey S. Rosenschein

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tactic 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 tactic coordination domains.

AAMAS Conference 2007 Conference Paper

An Adversarial Environment Model for Bounded Rational Agents in Zero-Sum Interactions

• Inon Zuckerman
• Sarit Kraus
• Jeffrey S. Rosenschein
• Gal Kaminka

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).