Publications (22)
Temporal Action-Graph Games: A New Representation for Dynamic Games
Albert Xin Jiang, Kevin Leyton-Brown, Avi Pfeffer
In this paper we introduce temporal action graph games (TAGGs), a novel graphical representation of imperfect-information extensive form games. We show that when a game involves an…
Sufficiency, Separability and Temporal Probabilistic Models
Avi Pfeffer
Suppose we are given the conditional probability of one variable given some other variables.Normally the full joint distribution over the conditioning variablesis required to deter…
Loopy Belief Propagation as a Basis for Communication in Sensor Networks
Christopher Crick, Avi Pfeffer
Sensor networks are an exciting new kind of computer system. Consisting of a large number of tiny, cheap computational devices physically distributed in an environment, they gather…
Factored Particles for Scalable Monitoring
Brenda Ng, Leonid Peshkin, Avi Pfeffer
Exact monitoring in dynamic Bayesian networks is intractable, so approximate algorithms are necessary. This paper presents a new family of approximate monitoring algorithms that co…
SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation
Avi Pfeffer, Daphne Koller, Brian Milch +1
In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling lan…
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations
Avi Pfeffer, Michael Harradon, Joseph Campolongo +1
We introduce Scruff, a new framework for developing AI systems using probabilistic programming. Scruff enables a variety of representations to be included, such as code with stocha…
Bayesian Information Extraction Network
Leonid Peshkin, Avi Pfeffer
Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs a…
Learning Game Representations from Data Using Rationality Constraints
Xi Alice Gao, Avi Pfeffer
While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representations from…
Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (2011)
Fabio Cozman, Avi Pfeffer
This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.
Asynchronous Dynamic Bayesian Networks
Avi Pfeffer, Terry Tai
Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These en…
Probabilistic Programming for Malware Analysis
Brian Ruttenberg, Lee Kellogg, Avi Pfeffer
Constructing lineages of malware is an important cyber-defense task. Performing this task is difficult, however, due to the amount of malware data and obfuscation techniques by the…
Decision-Making with Complex Data Structures using Probabilistic Programming
Brian E. Ruttenberg, Avi Pfeffer
Existing decision-theoretic reasoning frameworks such as decision networks use simple data structures and processes. However, decisions are often made based on complex data structu…
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
Avi Pfeffer, Brian Ruttenberg, William Kretschmer
Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference al…
Object-Oriented Bayesian Networks
Daphne Koller, Avi Pfeffer
Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applicatio…
Identifying reasoning patterns in games
Dimitrios Antos, Avi Pfeffer
We present an algorithm that identifies the reasoning patterns of agents in a game, by iteratively examining the graph structure of its Multi-Agent Influence Diagram (MAID) represe…
Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes
Yaakov Gal, Avi Pfeffer
This paper presents Networks of Influence Diagrams (NID), a compact, natural and highly expressive language for reasoning about agents beliefs and decision-making processes. NIDs a…
Approximate Separability for Weak Interaction in Dynamic Systems
Avi Pfeffer
One approach to monitoring a dynamic system relies on decomposition of the system into weakly interacting subsystems. An earlier paper introduced a notion of weak interaction calle…
Learning Probabilistic Programs Using Backpropagation
Avi Pfeffer
Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods. Howeve…
Learning and Solving Many-Player Games through a Cluster-Based Representation
Sevan G. Ficici, David C. Parkes, Avi Pfeffer
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…
Artificial Intelligence Based Malware Analysis
Avi Pfeffer, Brian Ruttenberg, Lee Kellogg +12
Artificial intelligence methods have often been applied to perform specific functions or tasks in the cyber-defense realm. However, as adversary methods become more complex and dif…
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…
Lazy Factored Inference for Functional Probabilistic Programming
Avi Pfeffer, Brian Ruttenberg, Amy Sliva +2
Probabilistic programming provides the means to represent and reason about complex probabilistic models using programming language constructs. Even simple probabilistic programs ca…