39 research outputs found
On the Treewidth of Dynamic Graphs
Dynamic graph theory is a novel, growing area that deals with graphs that
change over time and is of great utility in modelling modern wireless, mobile
and dynamic environments. As a graph evolves, possibly arbitrarily, it is
challenging to identify the graph properties that can be preserved over time
and understand their respective computability.
In this paper we are concerned with the treewidth of dynamic graphs. We focus
on metatheorems, which allow the generation of a series of results based on
general properties of classes of structures. In graph theory two major
metatheorems on treewidth provide complexity classifications by employing
structural graph measures and finite model theory. Courcelle's Theorem gives a
general tractability result for problems expressible in monadic second order
logic on graphs of bounded treewidth, and Frick & Grohe demonstrate a similar
result for first order logic and graphs of bounded local treewidth.
We extend these theorems by showing that dynamic graphs of bounded (local)
treewidth where the length of time over which the graph evolves and is observed
is finite and bounded can be modelled in such a way that the (local) treewidth
of the underlying graph is maintained. We show the application of these results
to problems in dynamic graph theory and dynamic extensions to static problems.
In addition we demonstrate that certain widely used dynamic graph classes
naturally have bounded local treewidth
Graph Neural Networks as the Copula Mundi between Logic and Machine Learning: A Roadmap
Combining machine learning (ML) and computational logic (CL) is hard, mostly because of the inherently- different ways they use to represent knowledge. In fact, while ML relies on fixed-size numeric repre- sentations leveraging on vectors, matrices, or tensors of real numbers, CL relies on logic terms and clauses—which are unlimited in size and structure.
Graph neural networks (GNN) are a novelty in the ML world introduced for dealing with graph- structured data in a sub-symbolic way. In other words, GNN pave the way towards the application of ML to logic clauses and knowledge bases. However, there are several ways to encode logic knowledge into graphs: which is the best one heavily depends on the specific task at hand.
Accordingly, in this paper, we (i) elicit a number of problems from the field of CL that may benefit from many graph-related problems where GNN has been proved effective; (ii) exemplify the application of GNN to logic theories via an end-to-end toy example, to demonstrate the many intricacies hidden behind the technique; (iii) discuss the possible future directions of the application of GNN to CL in general, pointing out opportunities and open issues
Recognizing complex faces and gaits via novel probabilistic models
In the field of computer vision, developing automated systems to recognize people
under unconstrained scenarios is a partially solved problem. In unconstrained sce-
narios a number of common variations and complexities such as occlusion, illumi-
nation, cluttered background and so on impose vast uncertainty to the recognition
process. Among the various biometrics that have been emerging recently, this
dissertation focus on two of them namely face and gait recognition.
Firstly we address the problem of recognizing faces with major occlusions amidst
other variations such as pose, scale, expression and illumination using a novel
PRObabilistic Component based Interpretation Model (PROCIM) inspired by key
psychophysical principles that are closely related to reasoning under uncertainty.
The model basically employs Bayesian Networks to establish, learn, interpret and
exploit intrinsic similarity mappings from the face domain. Then, by incorporating
e cient inference strategies, robust decisions are made for successfully recognizing
faces under uncertainty. PROCIM reports improved recognition rates over recent
approaches.
Secondly we address the newly upcoming gait recognition problem and show that
PROCIM can be easily adapted to the gait domain as well. We scienti cally
de ne and formulate sub-gaits and propose a novel modular training scheme to
e ciently learn subtle sub-gait characteristics from the gait domain. Our results
show that the proposed model is robust to several uncertainties and yields sig-
ni cant recognition performance. Apart from PROCIM, nally we show how a
simple component based gait reasoning can be coherently modeled using the re-
cently prominent Markov Logic Networks (MLNs) by intuitively fusing imaging,
logic and graphs.
We have discovered that face and gait domains exhibit interesting similarity map-
pings between object entities and their components. We have proposed intuitive
probabilistic methods to model these mappings to perform recognition under vari-
ous uncertainty elements. Extensive experimental validations justi es the robust-
ness of the proposed methods over the state-of-the-art techniques.
GOAL-DTU: Development of Distributed Intelligence for the Multi-Agent Programming Contest
We provide a brief description of the GOAL-DTU system for the agent contest,
including the overall strategy and how the system is designed to apply this
strategy. Our agents are implemented using the GOAL programming language. We
evaluate the performance of our agents for the contest, and finally also
discuss how to improve the system based on analysis of its strengths and
weaknesses.Comment: 28 pages, 45 figure