39 citations · 107 across the 30 of their papers we have counts for
16 papers · 1 filter
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs
David Tolpin, Tomer Dobkin
It is well known that reinforcement learning can be cast as inference in an appropriate probabilistic model. However, this commonly involves introducing a distribution over agent t…
A Planning Approach to Monitoring Behavior of Computer Programs
Alexandre Cukier, Ronen I. Brafman, Yotam Perkal +1
We describe a novel approach to monitoring high level behaviors using concepts from AI planning. Our goal is to understand what a program is doing based on its system call trace. T…
A Renewal Model of Intrusion
David Tolpin
We present a probabilistic model of an intrusion in a renewal process. Given a process and a sequence of events, an intrusion is a subsequence of events that is not produced by the…
Session Analysis using Plan Recognition
Reuth Mirsky, Ya'akov Gal, David Tolpin
This paper presents preliminary results of our work with a major financial company, where we try to use methods of plan recognition in order to investigate the interactions of a co…
Maximum a Posteriori Estimation by Search in Probabilistic Programs
David Tolpin, Frank Wood
We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Proba…
Path Finding under Uncertainty through Probabilistic Inference
David Tolpin, Brooks Paige, Jan Willem van de Meent +1
We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to…