1,721,065 research outputs found
Artificial intelligence tools for cyber attribution
No full textCyber attribution is the process by which the identity of an actor or aggressor in a cyberactivity is determined. Conducting this process presents several unique problems; chief among them are that the technical artifacts produced by cyberattacks are difficult to understand, and it is easy (and quite useful) for an actor to perform deception.Fil: Nunes, Eric. Arizona State University; Estados UnidosFil: Shakarian, Paulo. Arizona State University; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Ruef, Andrew. University of Maryland; Estados Unido
Data-driven Generation of Policies
No full textThis Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.Fil: Parker, Austin. University of Maryland; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. University of Oxford; Reino UnidoFil: Sliva, Amy. Charles River Analytics Inc.; Estados UnidosFil: Subrahmanian, Venkatramanan. University of Maryland; Estados Unido
Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems
Full text linkRecommender systems study the characteristics of its users and applying different kinds of processing to the available data, find a subset of items that may be of interest to a given user in a specific situation. Argumentation-based tools offer the possibility of analyzing complex and dynamic domains by generating and analyzing arguments for and against recommending a specific item based on the users' preferences. This approach allows us to analyze the qualitative and quantitative characteristics of the recommended items, and to provide explanations to increase transparency. In this article, we develop a set of software engineering guidelines for the analysis and design of recommender systems leveraging this approach.Fil: Leiva, Mario Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Budan, Maximiliano Celmo David. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y Tecnologías. Departamento de Matemática; ArgentinaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin
Inteligencia artificial: bases conceptuales para comprender la revolución de las revoluciones
No full textEl propósito principal de este ejercicio interdisciplinario consiste en presentar las herramientas básicas para que la inteligencia artificial esté al alcance de todo aquel que esté interesado en comprender o aplicar este gran conjunto de herramientas en su disciplina, en su vida diaria o en su organización. Ya sea desde el punto de vista de un/a investigador/a desarrollando su disciplina, un/a docente buscando comprender y enseñar el rol que la inteligencia artificial puede tener en su asignatura o una persona que ejerce su profesión dentro del gran conjunto de áreas que están siendo afectadas o lo serán en un futuro por ella...Fil: Corvalán, Juan Gustavo. No especifíca;Fil: Díaz Dávila, Laura C.. Universidad de Córdoba; EspañaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin
BEEF: Balanced English Explanations of Forecasts
No full textThe problem of understanding the reasons behind why different machine learning classifiers make specific predictions is a difficult one, mainly because the inner workings of the algorithms underlying such tools are not amenable to the direct extraction of succinct explanations. In this paper, we address the problem of automatically extracting balanced explanations from predictions generated by any classifier, which include not only why the prediction might be correct but also why it could be wrong. Our framework, called Balanced English Explanations of Forecasts, can generate such explanations in natural language. After showing that the problem of generating explanations is NP-complete, we focus on the development of a heuristic algorithm, empirically showing that it produces high-quality results both in terms of objective measures - with statistically significant effects shown for several parameter variations - and subjective evaluations based on a survey completed by 100 anonymous participants recruited via Amazon Mechanical Turk.Fil: Grover, Sachin. University of Carnegie Mellon; Estados UnidosFil: Pulice, Chiara. Dartmouth College; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Subrahmanian, Venkatramanan. Dartmouth College; Estados Unido
A Probabilistic Logic of Cyber Deception
Full text linkMalicious attackers often scan nodes in a network in order to identify vulnerabilities that they may exploit as they traverse the network. In this paper, we propose that the system generates a mix of true and false answers in response to scan requests. If the attacker believes that all scan results are true, then he will be on a wrong path. If he believes some scan results are faked, he would have to expend time and effort in order to separate fact from fiction. We propose a probabilistic logic of deception and show that various computations are NP-hard. We model the attacker’s state and show the effects of faked scan results. We then show how the defender can generate fake scan results in different states that minimize the damage the attacker can produce. We develop a Naive-PLD algorithm and a Fast-PLD heuristic algorithm for the defender to use and show experimentally that the latter performs well in a fraction of the run time of the former. We ran detailed experiments to assess the performance of these algorithms and further show that by running Fast-PLD off-line and storing the results, we can very efficiently answer run-time scan requests
An argument-based multi-agent system for information integration
Full text linkIn this paper we address the problem of obtaining a consolidated view of the knowledge that a community of information agents possesses in the form of private, possibly large, databases. Each agent in the community has independent sources of information and each database could contain information that is potentially inconsistent and incomplete, both by itself and/or in conjunction with some of the others. These characteristics make the consolidation difficult by traditional means. The idea of obtaining a single view is to provide a way of querying the resulting knowledge in a skeptical manner, i.e., receiving one answer that reflects the perception of the information community. Agents using the proposed system will be able to access multiple sources of knowledge represented in the form of deductive databases as if they were accessing a single one. One application of this schema is a novel architecture for decision-support systems (DSS) that will combine database technologies, specifically federated databases, which we will cast as information agents, with an argumentation-based framework. © 2011 Springer-Verlag.Fil: Capobianco, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin
A quantitative approach to belief revision in structured probabilistic argumentation
Full text linkMany real-world knowledge-based systems must deal with information coming from different sources that invariably leads to incompleteness, overspecification, or inherently uncertain content. The presence of these varying levels of uncertainty doesn’t mean that the information is worthless – rather, these are hurdles that the knowledge engineer must learn to work with. In this paper, we continue work on an argumentation-based framework that extends the well-known Defeasible Logic Programming (DeLP) language with probabilistic uncertainty, giving rise to the Defeasible Logic Programming with Presumptions and Probabilistic Environments (DeLP3E) model. Our prior work focused on the problem of belief revision in DeLP3E, where we proposed a non-prioritized class of revision operators called AFO (Annotation Function-based Operators) to solve this problem. In this paper, we further study this class and argue that in some cases it may be desirable to define revision operators that take quantitative aspects into account, such as how the probabilities of certain literals or formulas of interest change after the revision takes place. To the best of our knowledge, this problem has not been addressed in the argumentation literature to date. We propose the QAFO (Quantitative Annotation Function-based Operators) class of operators, a subclass of AFO, and then go on to study the complexity of several problems related to their specification and application in revising knowledge bases. Finally, we present an algorithm for computing the probability that a literal is warranted in a DeLP3E knowledge base, and discuss how it could be applied towards implementing QAFO-style operators that compute approximations rather than exact operations.Fil: Simari, Gerardo. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Shakarian, Paulo. Arizona State University; Estados UnidosFil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentin
On the incremental computation of semantics in dynamic argumentation
Full text linkArgumentation frameworks often model dynamic situations where arguments and their relationships (e.g., attacks) frequently change over time. As a consequence, the sets of conclusions (e.g., extensions of abstract argumentation frameworks, or warranted literals for structured argumentation frameworks) often need to be computed again after performing an update. However, as most of the argumentation semantics proposed so far suffer from high computational complexity, computing the set of conclusions from scratch is costly in general. In this work, we address the problems of efficiently recomputing extensions of dynamic abstract argumentation frameworks and warranted literals in dynamic defeasible knowledge bases. In particular, we first present an incremental algorithmic solution whose main idea is that of using an initial extension and the update to identify a (potentially small) portion of an abstract argumentation framework, which is sufficient to compute an extension of the updated framework.Fil: Gianvincenzo, Alfano. Università della Calabria; ItaliaFil: Greco, Sergio. Università della Calabria; ItaliaFil: Parisi, Francesco. Università della Calabria; ItaliaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin
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