Now showing items 1-17 of 17

    • Colored trails: A multiagent system testbed for decision-making research (demonstration) 

      Ficici, Sevan; Pfeffer, Avrom; Gal, Ya'akov Kobi; Grosz, Barbara; Shieber, Stuart (Association for Computing Machinery, 2008)
      With increasing frequency, computer agents participate in collaborative and competitive multiagent domains in which humans reason strategically to make decisions. The deployment of computer agents in such domains requires ...
    • The Design and Implementation of IBAL: A General-Purpose Probabilistic Language 

      Pfeffer, Avi (2005)
      This paper describes IBAL, a high level representation language for probabilistic AI. IBAL integrates several aspects of probability-based rational behavior, including probabilistic reasoning, Bayesian parameter estimation ...
    • Factored Reasoning for Monitoring Dynamic Team and Goal Formation 

      Pfeffer, Avrom; Das, Subrata; Lawless, David; Ng, Brenda (Elsevier, 2009)
    • Factored Sampling For Efficient Tracking of Large Hybrid Systems 

      Mg, Brenda; Pfeffer, Avi; Dearden, Richard (2005)
      This work presents a new approach to monitoring large dynamic systems. The approach is based on factored particles, which adapts particle filtering by factoring the system into weakly interacting subsystems and maintaining ...
    • A General Importance Sampling Algorithm for Probabilistic Programs 

      Pfeffer, Avi (2007)
      Highly expressive probabilistic modeling languages are capable of describing a wide variety of models. Some of these models are quite complex, so approximate inference algorithms are needed. One approach to approximate ...
    • Genetic Algorithm Optimization of Dynamic Support Vector Regression 

      Milnes, Thomas Bradford; Thorpe, Christopher; Pfeffer, Avi (2009)
      We show that genetic algorithms (GA) find optimized dynamic support vector machines (DSVMs) more efficiently than the grid search (GS) optimization approach. In addition, we show that GA-DSVMs find extremely low-error ...
    • Heuristics for Automatically Decomposing a Stochastic Process for Factored Inference 

      Frogner, Charlie; Pfeffer, Avi (2007)
      Dynamic Bayesian networks are factored representations of stochastic processes. Despite their factoredness, exact inference in DBNs is generally intractable. One approach to approximate inference involves factoring the ...
    • The Influence of Contexts on Decision-Making 

      Gal, Ya'akov; Grosz, Barbara J.; Pfeffer, Avi; Shieber, Stuart Merrill; Allain, Alex (2007)
      Many 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 ...
    • The influence of task contexts on the decision-making of humans and computers. 

      Gal, Ya'akov Kobi; Grosz, Barbara; Pfeffer, Avrom; Shieber, Stuart; Allain, Alex (Springer, 2007)
      Many environments in which people and computer agents interact involve deploying resources to accomplish tasks and satisfy goals. This paper investigates the way that the context in which decisions are made affects the ...
    • A Language for Descriptive Decision and Game Theory 

      Pfeffer, Avi; Gal, Ya'akov (2002)
      In 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 ...
    • Learning and Decision-Making for Intention Reconciliation 

      Das, Sanmay; Grosz, Barbara; Pfeffer, Avrom (Association for Computing Machinery, 2002)
      Rational, autonomous agents must be able to revise their commitments in the light of new opportunities. They must decide when to default on commitments to the group in order to commit to potentially more valuable outside ...
    • Learning and Solving Many-Player Games Through a Cluster-Based Representation 

      Ficici, Sevan; Parkes, David C.; Pfeffer, Avi J. (Association for Uncertainty in Artificial Intelligence, 2008)
      In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. A cluster groups together agents with a ...
    • Learning Social Preferences in Games 

      Gal, Ya'akov; Pfeffer, Avrom; Marzo, Francesca; Grosz, Barbara (Assocation for the Advancement of Artifical Intelligence, 2004)
      This paper presents a machine-learning approach to modeling human behavior in one-shot games. It provides a framework for representing and reasoning about the social factors that affect people’s play. The model predicts ...
    • Modeling how Humans Reason about Others with Partial Information 

      Ficici, Sevan G; Pfeffer, Avi (2007)
      Computer agents participate in many collaborative and competitive multi-agent domains in which humans make decisions. For computer agents to interact successfully with people in such environments, an understanding of human ...
    • Simultaneously Modeling Humans' Preferences and their Beliefs about Others' Preferences 

      Ficici, Sevan G.; Pfeffer, Avi (2007)
      In strategic multi-agent decision making, it is often the case that a strategic reasoner must hold beliefs about other agents and use these beliefs to inform its decision making. The behavior thus produced by the reasoner ...
    • Specifying and Monitoring Market Mechanisms Using Rights and Obligations 

      Michael, Loizos; Parkes, David C.; Pfeffer, Avi J. (Springer Verlag, 2005)
      We provide a formal scripting language to capture the semantics of market mechanisms. The language is based on a set of well-defined principles, and is designed to capture an agent’s rights, as derived from property, and ...