5,895 research outputs found
Using Generalized Annotated Programs to Solve Social Network Optimization Problems
Reasoning about social networks (labeled, directed, weighted graphs) is becoming increasingly important and there are now models of how certain phenomena (e.g. adoption of products/services by consumers, spread of a given disease) "diffuse" through the network. Some of these diffusion models can be expressed via generalized annotated programs (GAPs). In this paper, we consider the following problem: suppose we have a given goal to achieve (e.g. maximize the expected number of adoptees of a product or minimize the spread of a disease) and suppose we have limited resources to use in trying to achieve the goal (e.g. give out a few free plans, provide medication to key people in the SN) - how should these resources be used so that we optimize a given objective function related to the goal? We define a class of social network optimization problems (SNOPs) that supports this type of reasoning. We formalize and study the complexity of SNOPs and show how they can be used in conjunction with existing economic and disease diffusion models
Introduction to the 26th International Conference on Logic Programming Special Issue
This is the preface to the 26th International Conference on Logic Programming
Special IssueComment: 6 page
Spatio-Temporal Reasoning About Agent Behavior
There are many applications where we wish to reason about spatio-temporal aspects of an agent's behavior. This dissertation examines several facets of this type of reasoning. First, given a model of past agent behavior, we wish to reason about the probability that an agent takes a given action at a certain time. Previous work combining temporal and probabilistic reasoning has made either independence or Markov assumptions. This work introduces Annotated Probabilistic Temporal (APT) logic which makes neither assumption. Statements in APT logic consist of rules of the form "Formula G becomes true with a probability [L,U] within T time units after formula F becomes true'' and can be written by experts or extracted automatically. We explore the problem of entailment - finding the probability that an agent performs a given action at a certain time based on such a model. We study this problem's complexity and develop a sound, but incomplete fixpoint operator as a heuristic - implementing it and testing it on automatically generated models from several datasets.
Second, agent behavior often results in "observations'' at geospatial locations that imply the existence of other, unobserved, locations we wish to find ("partners"). In this dissertation, we formalize this notion with "geospatial abduction problems" (GAPs). GAPs try to infer a set of partner locations for a set of observations and a model representing the relationship between observations and partners for a given agent. This dissertation presents exact and approximate algorithms for solving GAPs as well as an implemented software package for addressing these problems called
SCARE (the Spatio-Cultural Abductive Reasoning Engine). We tested SCARE on counter-insurgency data from Iraq and obtained good results. We then provide an adversarial extension to GAPs as follows: given a fixed set of observations, if an adversary has probabilistic knowledge of how an agent were to find a corresponding set of partners, he would place the partners in locations that minimize the expected number of partners found by the agent. We examine this problem, along with its complement by studying their computational complexity, developing algorithms, and implementing approximation schemes.
We also introduce a class of problems called geospatial optimization problems (GOPs). Here the agent has a set of actions that modify attributes of a geospatial region and he wishes to select a limited number of such actions (with respect to some budget and other constraints) in a manner that maximizes a benefit function. We study the complexity of this problem and develop exact methods. We then develop an approximation algorithm with a guarantee. For some real-world applications, such as epidemiology, there is an underlying diffusion process that also affects geospatial proprieties. We address this with social network optimization problems (SNOPs) where given a weighted, labeled, directed graph we seek to find a set of vertices, that if given some initial property, optimize an aggregate study with respect to such diffusion. We develop and implement a heuristic that obtains a guarantee for a large class of such problems
Deep Network Flow for Multi-Object Tracking
Data association problems are an important component of many computer vision
applications, with multi-object tracking being one of the most prominent
examples. A typical approach to data association involves finding a graph
matching or network flow that minimizes a sum of pairwise association costs,
which are often either hand-crafted or learned as linear functions of fixed
features. In this work, we demonstrate that it is possible to learn features
for network-flow-based data association via backpropagation, by expressing the
optimum of a smoothed network flow problem as a differentiable function of the
pairwise association costs. We apply this approach to multi-object tracking
with a network flow formulation. Our experiments demonstrate that we are able
to successfully learn all cost functions for the association problem in an
end-to-end fashion, which outperform hand-crafted costs in all settings. The
integration and combination of various sources of inputs becomes easy and the
cost functions can be learned entirely from data, alleviating tedious
hand-designing of costs.Comment: Accepted to CVPR 201
MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)
The modeling of cascade processes in multi-agent systems in the form of
complex networks has in recent years become an important topic of study due to
its many applications: the adoption of commercial products, spread of disease,
the diffusion of an idea, etc. In this paper, we begin by identifying a
desiderata of seven properties that a framework for modeling such processes
should satisfy: the ability to represent attributes of both nodes and edges, an
explicit representation of time, the ability to represent non-Markovian
temporal relationships, representation of uncertain information, the ability to
represent competing cascades, allowance of non-monotonic diffusion, and
computational tractability. We then present the MANCaLog language, a formalism
based on logic programming that satisfies all these desiderata, and focus on
algorithms for finding minimal models (from which the outcome of cascades can
be obtained) as well as how this formalism can be applied in real world
scenarios. We are not aware of any other formalism in the literature that meets
all of the above requirements
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
Although fully generative models have been successfully used to model the
contents of text documents, they are often awkward to apply to combinations of
text data and document metadata. In this paper we propose a
Dirichlet-multinomial regression (DMR) topic model that includes a log-linear
prior on document-topic distributions that is a function of observed features
of the document, such as author, publication venue, references, and dates. We
show that by selecting appropriate features, DMR topic models can meet or
exceed the performance of several previously published topic models designed
for specific data.Comment: Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008
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