97,874 research outputs found
Game Theory Meets Network Security: A Tutorial at ACM CCS
The increasingly pervasive connectivity of today's information systems brings
up new challenges to security. Traditional security has accomplished a long way
toward protecting well-defined goals such as confidentiality, integrity,
availability, and authenticity. However, with the growing sophistication of the
attacks and the complexity of the system, the protection using traditional
methods could be cost-prohibitive. A new perspective and a new theoretical
foundation are needed to understand security from a strategic and
decision-making perspective. Game theory provides a natural framework to
capture the adversarial and defensive interactions between an attacker and a
defender. It provides a quantitative assessment of security, prediction of
security outcomes, and a mechanism design tool that can enable
security-by-design and reverse the attacker's advantage. This tutorial provides
an overview of diverse methodologies from game theory that includes games of
incomplete information, dynamic games, mechanism design theory to offer a
modern theoretic underpinning of a science of cybersecurity. The tutorial will
also discuss open problems and research challenges that the CCS community can
address and contribute with an objective to build a multidisciplinary bridge
between cybersecurity, economics, game and decision theory
Cheating for Problem Solving: A Genetic Algorithm with Social Interactions
We propose a variation of the standard genetic algorithm that incorporates
social interaction between the individuals in the population. Our goal is to
understand the evolutionary role of social systems and its possible application
as a non-genetic new step in evolutionary algorithms. In biological
populations, ie animals, even human beings and microorganisms, social
interactions often affect the fitness of individuals. It is conceivable that
the perturbation of the fitness via social interactions is an evolutionary
strategy to avoid trapping into local optimum, thus avoiding a fast convergence
of the population. We model the social interactions according to Game Theory.
The population is, therefore, composed by cooperator and defector individuals
whose interactions produce payoffs according to well known game models
(prisoner's dilemma, chicken game, and others). Our results on Knapsack
problems show, for some game models, a significant performance improvement as
compared to a standard genetic algorithm.Comment: 7 pages, 5 Figures, 5 Tables, Proceedings of Genetic and Evolutionary
Computation Conference (GECCO 2009), Montreal, Canad
Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences
This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering
Parameterizing by the Number of Numbers
The usefulness of parameterized algorithmics has often depended on what
Niedermeier has called, "the art of problem parameterization". In this paper we
introduce and explore a novel but general form of parameterization: the number
of numbers. Several classic numerical problems, such as Subset Sum, Partition,
3-Partition, Numerical 3-Dimensional Matching, and Numerical Matching with
Target Sums, have multisets of integers as input. We initiate the study of
parameterizing these problems by the number of distinct integers in the input.
We rely on an FPT result for ILPF to show that all the above-mentioned problems
are fixed-parameter tractable when parameterized in this way. In various
applied settings, problem inputs often consist in part of multisets of integers
or multisets of weighted objects (such as edges in a graph, or jobs to be
scheduled). Such number-of-numbers parameterized problems often reduce to
subproblems about transition systems of various kinds, parameterized by the
size of the system description. We consider several core problems of this kind
relevant to number-of-numbers parameterization. Our main hardness result
considers the problem: given a non-deterministic Mealy machine M (a finite
state automaton outputting a letter on each transition), an input word x, and a
census requirement c for the output word specifying how many times each letter
of the output alphabet should be written, decide whether there exists a
computation of M reading x that outputs a word y that meets the requirement c.
We show that this problem is hard for W[1]. If the question is whether there
exists an input word x such that a computation of M on x outputs a word that
meets c, the problem becomes fixed-parameter tractable
A 2.75-Approximation Algorithm for the Unconstrained Traveling Tournament Problem
A 2.75-approximation algorithm is proposed for the unconstrained traveling
tournament problem, which is a variant of the traveling tournament problem. For
the unconstrained traveling tournament problem, this is the first proposal of
an approximation algorithm with a constant approximation ratio. In addition,
the proposed algorithm yields a solution that meets both the no-repeater and
mirrored constraints. Computational experiments show that the algorithm
generates solutions of good quality.Comment: 12 pages, 1 figur
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