45 research outputs found
Maximum a Posteriori Estimation by Search in Probabilistic Programs
We introduce an approximate search algorithm for fast maximum a posteriori
probability estimation in probabilistic programs, which we call Bayesian ascent
Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with
varying number of mutually dependent finite, countable, and continuous random
variables. BaMC is an anytime MAP search algorithm applicable to any
combination of random variables and dependencies. We compare BaMC to other MAP
estimation algorithms and show that BaMC is faster and more robust on a range
of probabilistic models.Comment: To appear in proceedings of SOCS1
Rational Value of Information Estimation for Measurement Selection
Computing value of information (VOI) is a crucial task in various aspects of
decision-making under uncertainty, such as in meta-reasoning for search; in
selecting measurements to make, prior to choosing a course of action; and in
managing the exploration vs. exploitation tradeoff. Since such applications
typically require numerous VOI computations during a single run, it is
essential that VOI be computed efficiently. We examine the issue of anytime
estimation of VOI, as frequently it suffices to get a crude estimate of the
VOI, thus saving considerable computational resources. As a case study, we
examine VOI estimation in the measurement selection problem. Empirical
evaluation of the proposed scheme in this domain shows that computational
resources can indeed be significantly reduced, at little cost in expected
rewards achieved in the overall decision problem.Comment: 7 pages, 2 figures, presented at URPDM2010; plots fixe
Rational Deployment of CSP Heuristics
Heuristics are crucial tools in decreasing search effort in varied fields of
AI. In order to be effective, a heuristic must be efficient to compute, as well
as provide useful information to the search algorithm. However, some well-known
heuristics which do well in reducing backtracking are so heavy that the gain of
deploying them in a search algorithm might be outweighed by their overhead.
We propose a rational metareasoning approach to decide when to deploy
heuristics, using CSP backtracking search as a case study. In particular, a
value of information approach is taken to adaptive deployment of solution-count
estimation heuristics for value ordering. Empirical results show that indeed
the proposed mechanism successfully balances the tradeoff between decreasing
backtracking and heuristic computational overhead, resulting in a significant
overall search time reduction.Comment: 7 pages, 2 figures, to appear in IJCAI-2011, http://www.ijcai.org
Process Monitoring on Sequences of System Call Count Vectors
We introduce a methodology for efficient monitoring of processes running on
hosts in a corporate network. The methodology is based on collecting streams of
system calls produced by all or selected processes on the hosts, and sending
them over the network to a monitoring server, where machine learning algorithms
are used to identify changes in process behavior due to malicious activity,
hardware failures, or software errors. The methodology uses a sequence of
system call count vectors as the data format which can handle large and varying
volumes of data.
Unlike previous approaches, the methodology introduced in this paper is
suitable for distributed collection and processing of data in large corporate
networks. We evaluate the methodology both in a laboratory setting on a
real-life setup and provide statistics characterizing performance and accuracy
of the methodology.Comment: 5 pages, 4 figures, ICCST 201