Person: Gal, Ya'akov
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Gal, Ya'akov
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Publication An Adaptive Agent for Negotiating with People in Different Cultures(Association for Computing Machinery, 2011) Gal, Ya'akov; Kraus, Sarit; Gelfand, Michele; Khashan, Hilal; Salmon, Elizabeth; Gal, YThe rapid dissemination of technology such as the Internet across geographical and ethnic lines is opening up opportunities for computer agents to negotiate with people of diverse cultural and organizational affiliations. To negotiate proficiently with people in different cultures, agents need to be able to adapt to the way behavioral traits of other participants change over time. This article describes a new agent for repeated bilateral negotiation that was designed to model and adapt its behavior to the individual traits exhibited by its negotiation partner. The agent’s decision-making model combined a social utility function that represented the behavioral traits of the other participant, as well as a rule-based mechanism that used the utility function to make decisions in the negotiation process. The agent was deployed in a strategic setting in which both participants needed to complete their individual tasks by reaching agreements and exchanging resources, the number of negotiation rounds was not fixed in advance and agreements were not binding. The agent negotiated with human subjects in the United States and Lebanon in situations that varied the dependency relationships between participants at the onset of negotiation. There was no prior data available about the way people would respond to different negotiation strategies in these two countries. Results showed that the agent was able to adopt a different negotiation strategy to each country. Its average performance across both countries was equal to that of people. However, the agent outperformed people in the United States, because it learned to make offers that were likely to be accepted by people, while being more beneficial to the agent than to people. In contrast, the agent was outperformed by people in Lebanon, because it adopted a high reliability measure which allowed people to take advantage of it. These results provide insight for human-computer agent designers in the types of multicultural settings that we considered, showing that adaptation is a viable approach towards the design of computer agents to negotiate with people when there is no prior data of their behavior.Publication Modeling User Perception of Interaction Opportunities for Effective Teamwork(IEEE, 2009) Kamar, Ece; Gal, Ya'akov; Grosz, BarbaraThis paper presents a model of collaborative decision-making for groups that involve people and computer agents. The model distinguishes between actions relating to participantspsila commitment to the group and actions relating to their individual tasks, uses this distinction to decompose group decision making into smaller problems that can be solved efficiently. It allows computer agents to reason about the benefits of their actions on a collaboration and the ways in which human participants perceive these benefits. The model was tested in a setting in which computer agents need to decide whether to interrupt people to obtain potentially valuable information. Results show that the magnitude of the benefit of interruption to the collaboration is a major factor influencing the likelihood that people will accept interruption requests. They further establish that peoplepsilas perceived type of their partners (whether humans or computers) significantly affected their perceptions of the usefulness of interruptions when the benefit of the interruption is not clear-cut. These results imply that system designers need to consider not only the possible benefits of interruptions to collaborative human-computer teams but also the way that such benefits are perceived by people.Publication A Language for Descriptive Decision and Game Theory(2002) Pfeffer, Avi; Gal, Ya'akovIn descriptive decision and game theory, one specifies a model of a situation faced by agents and uses the model to predict or explain their behavior. We present Influence Diagram Networks, a language for descriptive decision and game theory that is based on graphical models. Our language relaxes the assumption traditionally used in economics that beliefs of agents are consistent, i.e. conditioned on a common prior distribution. In the single-agent case one can model situations in which the agent has an incorrect model of the way the world works, or in which a modeler has uncertainty about the agent's model. In the multi-agent case, one can model agents' uncertain beliefs about other agents' decision-making models. We present an algorithm that computes the actions of agents under the assumption that they are rational with respect to their own model, but not necessarily with respect to the real world. Applications of our language include determining the cost to an agent of using an incorrect model, opponent modeling in games, and modeling bounded rationality.Publication The Influence of Contexts on Decision-Making(2007) Gal, Ya'akov; Grosz, Barbara; Pfeffer, Avi; Shieber, Stuart; Allain, AlexMany environments in which people and computer agents interact involve deploying resources to accomplish tasks and satisfy goals. This paper investigates the way that the contextual setting in which decisions are made affects the behavior of people and the performance of computer agents that interact with people in such environments. It presents experiments that measured negotiation behavior in two types of contextual settings. One provided a task context that made explicit the relationship between goals, tasks and resources, The other provided a completely abstract context in which the payoffs for all decision choices were listed. Results show that people are more helpful, less selfish, and less competitive when making decisions in task contexts than when making them in completely abstract contexts. Further, their overall performance was better in task contexts. A predictive computational model that was trained on data obtained in task contexts outperformed a model that was trained under abstract contexts. These results indicate that modeling the way people make decisions in context is essential for the design of computer agents that will interact with people.Publication Adapting to Agents' Personalities in Negotiation(2005) Gal, Ya'akov; Talman, Shavit; Hadad, Meirav; Kraus, SaritTo establish cooperative relationships, agents must be willing to engage in helpful behavior and to keep their commitments with agents who reciprocate this behavior. However, in uncertain and dynamic environments, it is difficult to identify the degree of helpfulness of other agents. This paper approaches this problem by characterizing agents’ helpfulness in terms of cooperation and reliability. An agent chooses an action based on other agents’ helpfulness as well as the dependency relationship between the agent and others. This model was evaluated in a negotiation game in which players needed to exchange resources to reach their goals, but did not have information about each other’s resources. Results showed that the model allowed agents to identify and to adapt to others’ varying degree of helpfulness, even while they are constantly changing their strategy. Moreover, agents who vary their cooperativeness and reliability depending on those traits of others, can outperform agents who do not, as well as increase the social welfare of the group.Publication Modeling Agents' Beliefs using Networks of Influence Diagrams(2003) Gal, Ya'akov; Pfeffer, AviPublication Modeling Information Exchange Opportunities for Effective Human-Computer Teamwork(Elsevier, 2013) Kamar, Ece; Gal, Ya'akov; Grosz, BarbaraThis paper studies information exchange in collaborative group activities involving mixed networks of people and computer agents. It introduces the concept of "nearly decomposable" decision-making problems to address the complexity of information exchange decisions in such multi-agent settings. This class of decision-making problems arise in settings which have an action structure that requires agents to reason about only a subset of their partners' actions – but otherwise allows them to act independently. The paper presents a formal model of nearly decomposable decision-making problems, NED-MDPs, and defines an approximation algorithm, NED-DECOP that computes efficient information exchange strategies. The paper shows that NED-DECOP is more efficient than prior collaborative planning algorithms for this class of problem. It presents an empirical study of the information exchange decisions made by the algorithm that investigates the extent to which people accept interruption requests from a computer agent. The context for the study is a game in which the agent can ask people for information that may benefit its individual performance and thus the groupʼs collaboration. This study revealed the key factors affecting peopleʼs perception of the benefit of interruptions in this setting. The paper also describes the use of machine learning to predict the situations in which people deviate from the strategies generated by the algorithm, using a combination of domain features and features informed by the algorithm. The methodology followed in this work could form the basis for designing agents that effectively exchange information in collaborations with people.Publication Agent Decision-Making in Open Mixed Networks(Elsevier, 2010) Gal, Ya'akov; Grosz, Barbara; Kraus, Sarit; Shieber, StuartComputer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, and strategies that people bring into their interactions. The studies deploy a configurable system called Colored Trails (CT) that generates a family of games. CT is an abstract, conceptually simple but highly versatile game in which players negotiate and exchange resources to enable them to achieve their individual or group goals. It provides a realistic analogue to multi-agent task domains, while not requiring extensive domain modeling. It is less abstract than payoff matrices, and people exhibit less strategic and more helpful behavior in CT than in the identical payoff matrix decision-making context. By not requiring extensive domain modeling, CT enables agent researchers to focus their attention on strategy design, and it provides an environment in which the influence of social factors can be better isolated and studied.Publication Recognition of Users' Activities using Constraint Satisfaction(Springer, 2009) Reddy, Swapna Cherukupalli; Gal, Ya'akov; Shieber, StuartIdeally designed software allow users to explore and pursue interleaving plans, making it challenging to automatically recognize user interactions. The recognition algorithms presented use constraint satisfaction techniques to compare user interaction histories to a set of ideal solutions. We evaluate these algorithms on data obtained from user interactions with a commercially available pedagogical software, and find that these algorithms identified users’ activities with 93% accuracy.Publication Economic Games on the Internet: The Effect of $1 Stakes(Public Library of Science, 2012) Amir, Ofra; Rand, David Gertler; Gal, Ya'akovOnline labor markets such as Amazon Mechanical Turk (MTurk) offer an unprecedented opportunity to run economic game experiments quickly and inexpensively. Using Mturk, we recruited 756 subjects and examined their behavior in four canonical economic games, with two payoff conditions each: a stakes condition, in which subjects' earnings were based on the outcome of the game (maximum earnings of $1); and a no-stakes condition, in which subjects' earnings are unaffected by the outcome of the game. Our results demonstrate that economic game experiments run on MTurk are comparable to those run in laboratory settings, even when using very low stakes.