138 research outputs found
A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems
Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains
Towards Believable Resource Gathering Behaviours in Real-time Strategy Games with a Memetic Ant Colony System
AbstractIn this paper, the resource gathering problem in real-time strategy (RTS) games, is modeled as a path-finding problem where game agents responsible for gathering resources, also known as harvesters, are only equipped with the knowledge of its immediate sur- roundings and must gather knowledge about the dynamics of the navigation graph that it resides on by sharing information and cooperating with other agents in the game environment. This paper proposed the conceptual modeling of a memetic ant colony system (MACS) for believable resource gathering in RTS games. In the proposed MACS, the harvester's path-finding and resource gathering knowledge captured are extracted and represented as memes, which are internally encoded as state transition rules (mem- otype), and externally expressed as ant pheromone on the graph edge (sociotype). Through the inter-play between the memetic evolution and ant colony, harvesters as memetic automatons spawned from an ant colony are able to acquire increasing level of capability in exploring complex dynamic game environment and gathering resources in an adaptive manner, producing consistent and impressive resource gathering behaviors
Structured Memetic Automation for Online Human-like Social Behavior Learning
Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior
Homo Socionicus: a Case Study of Simulation Models of Norms
This paper describes a survey of normative agent-based social simulation models. These models are examined from the perspective of the foundations of social theory. Agent-based modelling contributes to the research program of methodological individualism. Norms are a central concept in the role theoretic concept of action in the tradition of Durkheim and Parsons. This paper investigates to what extend normative agent-based models are able to capture the role theoretic concept of norms. Three methodological core problems are identified: the question of norm transmission, normative transformation of agents and what kind of analysis the models contribute. It can be shown that initially the models appeared only to address some of these problems rather than all of them simultaneously. More recent developments, however, show progress in that direction. However, the degree of resolution of intra agent processes remains too low for a comprehensive understanding of normative behaviour regulation.Norms, Normative Agent-Based Social Simulation, Role Theory, Methodological Individualism
Preventing premature convergence and proving the optimality in evolutionary algorithms
http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality
Modélisation formelle des systÚmes de détection d'intrusions
LâĂ©cosystĂšme de la cybersĂ©curitĂ© Ă©volue en permanence en termes du nombre, de la diversitĂ©, et de la complexitĂ© des attaques. De ce fait, les outils de dĂ©tection deviennent inefficaces face Ă certaines attaques. On distingue gĂ©nĂ©ralement trois types de systĂšmes de dĂ©tection dâintrusions : dĂ©tection par anomalies, dĂ©tection par signatures et dĂ©tection hybride. La dĂ©tection par anomalies est fondĂ©e sur la caractĂ©risation du comportement habituel du systĂšme, typiquement de maniĂšre statistique. Elle permet de dĂ©tecter des attaques connues ou inconnues, mais gĂ©nĂšre aussi un trĂšs grand nombre de faux positifs. La dĂ©tection par signatures permet de dĂ©tecter des attaques connues en dĂ©finissant des rĂšgles qui dĂ©crivent le comportement connu dâun attaquant. Cela demande une bonne connaissance du comportement de lâattaquant. La dĂ©tection hybride repose sur plusieurs mĂ©thodes de dĂ©tection incluant celles sus-citĂ©es. Elle prĂ©sente lâavantage dâĂȘtre plus prĂ©cise pendant la dĂ©tection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour lâexpression de rĂšgles de reconnaissance dâattaques. Le nombre dâattaques potentielles Ă©tant trĂšs grand, ces bases de rĂšgles deviennent rapidement difficiles Ă gĂ©rer et Ă maintenir. De plus, lâexpression de rĂšgles avec Ă©tat dit stateful est particuliĂšrement ardue pour reconnaĂźtre une sĂ©quence dâĂ©vĂ©nements. Dans cette thĂšse, nous proposons une approche stateful basĂ©e sur les diagrammes dâĂ©tat-transition algĂ©briques (ASTDs) afin dâidentifier des attaques complexes. Les ASTDs permettent de reprĂ©senter de façon graphique et modulaire une spĂ©cification, ce qui facilite la maintenance et la comprĂ©hension des rĂšgles. Nous Ă©tendons la notation ASTD avec de nouvelles fonctionnalitĂ©s pour reprĂ©senter des attaques complexes. Ensuite, nous spĂ©cifions plusieurs attaques avec la notation Ă©tendue et exĂ©cutons les spĂ©cifications obtenues sur des flots dâĂ©vĂ©nements Ă lâaide dâun interprĂ©teur pour identifier des attaques. Nous Ă©valuons aussi les performances de lâinterprĂ©teur avec des outils industriels tels que Snort et Zeek. Puis, nous rĂ©alisons un compilateur afin de gĂ©nĂ©rer du code exĂ©cutable Ă partir dâune spĂ©cification ASTD, capable dâidentifier de façon efficiente les sĂ©quences dâĂ©vĂ©nements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity,
and the complexity of cyber attacks. Generally, we have three types of Intrusion
Detection System (IDS) : anomaly-based detection, signature-based detection, and
hybrid detection. Anomaly detection is based on the usual behavior description of
the system, typically in a static manner. It enables detecting known or unknown attacks
but also generating a large number of false positives. Signature based detection
enables detecting known attacks by defining rules that describe known attackerâs behavior.
It needs a good knowledge of attacker behavior. Hybrid detection relies on
several detection methods including the previous ones. It has the advantage of being
more precise during detection. Tools like Snort and Zeek offer low level languages to
represent rules for detecting attacks. The number of potential attacks being large,
these rule bases become quickly hard to manage and maintain. Moreover, the representation
of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition
diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular
representation of a specification, that facilitates maintenance and understanding of
rules. We extend the ASTD notation with new features to represent complex attacks.
Next, we specify several attacks with the extended notation and run the resulting specifications
on event streams using an interpreter to identify attacks. We also evaluate
the performance of the interpreter with industrial tools such as Snort and Zeek. Then,
we build a compiler in order to generate executable code from an ASTD specification,
able to efficiently identify sequences of events
Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society
Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with âreversible behaviorâ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of âclassicalâ states that determine molecular dynamics subject to Boltzmann statistics and âsteady-stateâ, metabolic (multi-stable) manifolds, together with âconfigurationâ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the âstandardâ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell âcycleâ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud
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KEYWORDS: Emergence of Life and Human Consciousness;\ud
Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell âcyclingâ; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patientsâ possible improvements of the designs for future clinical trials and cancer treatments. \ud
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