44,965 research outputs found
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant
regularities in a given dataset, determining at the same time the set of
configurations of the adopted parametric dissimilarity measure yielding compact
and separated clusters. Each agent operates independently by performing a
Markovian random walk on a suitable weighted graph representation of the input
dataset. Such a weighted graph representation is induced by the specific
parameter configuration of the dissimilarity measure adopted by the agent,
which searches and takes decisions autonomously for one cluster at a time.
Results show that the algorithm is able to discover parameter configurations
that yield a consistent and interpretable collection of clusters. Moreover, we
demonstrate that our algorithm shows comparable performances with other similar
state-of-the-art algorithms when facing specific clustering problems
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
Proceedings of the 11th European Agent Systems Summer School Student Session
This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz
Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions
In this article, we study the nonlinear Fokker-Planck (FP) equation that
arises as a mean-field (macroscopic) approximation of bounded confidence
opinion dynamics, where opinions are influenced by environmental noises and
opinions of radicals (stubborn individuals). The distribution of radical
opinions serves as an infinite-dimensional exogenous input to the FP equation,
visibly influencing the steady opinion profile. We establish mathematical
properties of the FP equation. In particular, we (i) show the well-posedness of
the dynamic equation, (ii) provide existence result accompanied by a
quantitative global estimate for the corresponding stationary solution, and
(iii) establish an explicit lower bound on the noise level that guarantees
exponential convergence of the dynamics to stationary state. Combining the
results in (ii) and (iii) readily yields the input-output stability of the
system for sufficiently large noises. Next, using Fourier analysis, the
structure of opinion clusters under the uniform initial distribution is
examined. Specifically, two numerical schemes for identification of
order-disorder transition and characterization of initial clustering behavior
are provided. The results of analysis are validated through several numerical
simulations of the continuum-agent model (partial differential equation) and
the corresponding discrete-agent model (interacting stochastic differential
equations) for a particular distribution of radicals
A game theory framework for clustering
The Game Theory-based Multi-Agent System (GTMAS) of Toreyen and Salhi, [10] and [12], implements a loosely coupled hybrid algorithm that may involve any number of algorithms suitable, a priori, for the solution of a given optimisation problem. The system allows the available algorithms to co-operate toward the solution of the problem in hand as well as compete for the computing facilities they require to run. This co-operative/competitive aspect is captured through the implementation of the Prisoners? Dilemma paradigm of game theory. Here, we apply GTMAS to the problem of clustering European Union (EU) economies, including Turkey, to find out whether the latter, based on a number of criteria, can fit in the EU and find out which countries, if any, it has strong similaries with. This clustering problem is first converted into an optimisation problem, namely the Travelling Salesman Problem (TSP) before being solved with GTMAS involving two players (agents) each implementing a standard combinatorial optimisation algorithm. Computational results are included
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