1,721,100 research outputs found
Stratified Belief Bases Revision with Argumentative Inference
Get PDFWe propose a revision operator on a stratified belief base, i.e., a belief base that stores beliefs in different strata corresponding to the value an agent assigns to these beliefs. Furthermore, the operator will be defined as to perform the revision in such a way that information is never lost upon revision but stored in a stratum or layer containing information perceived as having a lower value. In this manner, if the revision of one layer leads to the rejection of some information to maintain consistency, instead of being withdrawn it will be kept and introduced in a different layer with lower value. Throughout this development we will follow the principle of minimal change, being one of the important principles proposed in belief change theory, particularly emphasized in the AGM model. Regarding the reasoning part from the stratified belief base, the agent will obtain the inferences using an argumentative formalism. Thus, the argumentation framework will decide which information prevails when sentences of different layers are used for entailing conflicting beliefs. We will also illustrate how inferences are changed and how the status of arguments can be modified after a revision process.Fil: Falappa, Marcelo Alejandro. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Garcia, Alejandro Javier. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kern Isberner, Gabriele. Universitat Dortmund; AlemaniaFil: Simari, Guillermo Ricardo. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
A Family of Decidable Bi-intuitionistic Modal Logics
No full textWe investigate intuitionistic logics extended with both the
co-implication connective of Hilbert–Brouwer logic and with
diamond and box modalities. We use a Kripke semantics based
on frames with two ‘forth’ confluence conditions on the modal
relation with respect to the intuitionistic relation. We give
sound and strongly complete axiomatisations for entailment
on this class of frames, and give similar axiomatisations for the
subclasses of frames satisfying any combination of reflexivity,
transitivity, and seriality. We then prove that all of these
logics are decidable, by proving that they have the finite frame
property
One knowledge graph to rule them all? Analyzing the differences between DBpedia, YAGO, Wikidata & co.
No full textPrioritized and Non-prioritized Multiple Change on Belief Bases
Get PDFIn this article we explore multiple change operators, i.e., operators in which the epistemic input is a set of sentences instead of a single sentence. We propose two types of change: prioritized change, in which the input set is fully accepted, and symmetric change, where both the epistemic state and the epistemic input are equally treated. In both kinds of operators we propose a set of postulates and we present different constructions: kernel changes and partial meet changes.Fil: 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; ArgentinaFil: Kern Isberner, Gabriele. Universitat Dortmund; AlemaniaFil: Reis, Maurício D. L.. Universidade da Madeira; PortugalFil: 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
Comprehensible knowledge base extraction for learning agents : practical challenges and applications in games
No full textThe need for artificial intelligence systems that are not only capable of mastering complicated tasks but also of explaining their decisions has massively gained attention over the last years. This also seems to offer opportunities for further interconnecting different approaches to artificial intelligence, such as machine learning and knowledge representation.
This work considers the task of learning knowledge bases from agent behavior, with a focus on human-readability, comprehensibility and applications in games. In this context, it will be presented how knowledge can be organized and processed on multiple levels of abstraction, allowing for efficient reasoning and revision. It will be investigated how learning agents can benefit from incorporating the approaches into their learning processes.
Examples and applications are provided, e.g., in the context of general video game playing. The most essential approaches are implemented in the InteKRator toolbox and show potential for being applied in other domains (e.g., in medical informatics).194 Seite
Distributing Knowledge Into Simple Bases
No full textUnderstanding the behavior of belief change operators for fragments of classical logic has received increasing interest over the last years. Results in this direction are mainly concerned with adapting representation theorems. However, fragment-driven belief change also leads to novel research questions. In this paper we propose the concept of belief distribution, which can be understood as the reverse task of merging. More specifically, we are interested in the following question: given an arbitrary knowledge base K and some merging operator Δ, can we find a profile E and a constraint μ, both from a given fragment of classical logic, such that Δμ(E) yields a result equivalent to K? In other words, we are interested in seeing if K can be distributed into knowledge bases of simpler structure, such that the task of merging allows for a reconstruction of the original knowledge. Our initial results show that merging based on drastic distance allows for an easy distribution of knowledge, while the power of distribution for operators based on Hamming distance relies heavily on the fragment of choice
A framework for inference control in incomplete logic databases
Get PDFSecurity in information systems aims at various, possibly conflicting goals, two of which are availablility and confidentiality. On the one hand, as much information as possible should be provided to the user. On the other hand, certain information may be confidential and must not be disclosed. In this context, inferences are a major problem: The user might combine a priori knowledge and public information gained from the answers in order to infer secret information.
Controlled Query Evaluation (CQE) is a dynamic, policy-driven mechanism for the
enforcement of confidentiality in information systems, namely by the distortion of certain
answers, by means of either lying or refusal. CQE prevents harmful inferences, and tries
to provide the best possible availability while still preserving confidentiality. In this thesis, we present a framework for Controlled Query Evaluation in incomplete logic databases. In the first part of the thesis, we consider CQE from a declarative point of view. We present three different types of confidentiality policy languages with different simplicity and expressibility – propositional potential secrets, confidentiality targets, and epistemic
potential secrets – and show how they relate to each other. We also give a formal, declarative definition of the requirements for a method protecting these types of policies. As it turns out, epistemic potential secrets are the most expressive policies of the three types studied, so we concentrate on these policies in the second part of the thesis. In that second part, we show how to operationally enforce confidentiality policies based on epistemic potential secrets. We first present an abstract framework in which two parameters
are left open: 1. Does the user know the elements of the confidentiality policy?
2. Do we allow only refusal, only lying, or both distortion methods? For five of the six
resulting cases, we present instantiations of the framework and prove the confidentiality
according to the declarative definition from the first part of the thesis. For the remaining case (combined lying and refusal under unknown policies), we show that no suitable enforcement method can be constructed using the naive heuristics.
Finally, we compare the enforcement methods to those constructed for complete
databases in earlier work, and we discuss the properties of our algorithms when relaxing
the assumptions about the user’s computational abilities.Security in information systems aims at various, possibly conflicting goals, two of which
are availablility and confidentiality. On the one hand, as much information as possible
should be provided to the user. On the other hand, certain information may be confidential
and must not be disclosed. In this context, inferences are a major problem: The user might
combine a priori knowledge and public information gained from the answers in order to
infer secret information.
Controlled Query Evaluation (CQE) is a dynamic, policy-driven mechanism for the
enforcement of confidentiality in information systems, namely by the distortion of certain
answers, by means of either lying or refusal. CQE prevents harmful inferences, and tries
to provide the best possible availability while still preserving confidentiality. In this thesis,
we present a framework for Controlled Query Evaluation in incomplete logic databases.
In the first part of the thesis, we consider CQE from a declarative point of view. We
present three different types of confidentiality policy languages with different simplicity
and expressibility – propositional potential secrets, confidentiality targets, and epistemic
potential secrets – and show how they relate to each other. We also give a formal, declarative
definition of the requirements for a method protecting these types of policies. As it
turns out, epistemic potential secrets are the most expressive policies of the three types
studied, so we concentrate on these policies in the second part of the thesis.
In that second part, we show how to operationally enforce confidentiality policies based
on epistemic potential secrets. We first present an abstract framework in which two parameters
are left open: 1. Does the user know the elements of the confidentiality policy?
2. Do we allow only refusal, only lying, or both distortion methods? For five of the six
resulting cases, we present instantiations of the framework and prove the confidentiality
according to the declarative definition from the first part of the thesis. For the remaining
case (combined lying and refusal under unknown policies), we show that no suitable
enforcement method can be constructed using the naive heuristics.
Finally, we compare the enforcement methods to those constructed for complete
databases in earlier work, and we discuss the properties of our algorithms when relaxing
the assumptions about the user’s computational abilities
Belief change operations under confidentiality requirements in multiagent systems
Get PDFMultiagent systems are populated with autonomous computing entities called agents which pro-actively pursue their goals.
The design of such systems is an active field within artificial intelligence research with one objective being flexible and adaptive
agents in dynamic and inaccessible environments.
An agent's decision-making and finally its success in achieving its goals crucially depends on the agent's information about its environment
and the sharing of information with other agents in the multiagent system. For this and other reasons, an agent's information is a valuable asset
and thus the agent is often interested in the confidentiality of parts of this information. From research in computer security it is well-known that
confidentiality is not only achieved by the agent's control of access to its data, but by its control of the flow of information when processing the data
during the interaction with other agents.
This thesis investigates how to specify and enforce the confidentiality interests of an agent D while it reacts to iterated query, revision
and update requests from another agent A for the purpose of information sharing.
First, we will enable the agent D to specify in a dedicated confidentiality policy that parts of its previous or current belief about its environment
should be hidden from the other requesting agent A.
To formalize the requirement of hiding belief, we will in particular postulate agent A's capabilities for reasoning about D's belief and about
D's processing of information to form its belief. Then, we will relate the requirements imposed by a confidentiality policy to others in the research
of information flow control and inference control in computer security.
Second, we will enable the agent D to enforce its confidentiality aims as expressed by its policy by refusing requests from A at a potential violation
of its policy. A crucial part of the enforcement is D's simulation of A's postulated reasoning about D's belief and the change of this belief.
In this thesis, we consider two particular operators of belief change: an update operator for a simple logic-oriented database model
and a revision operator for D's assertions about its environment that yield the agent's belief after its nonmonotonic reasoning.
To prove the effectiveness of D's means of enforcement, we study necessary properties of D's simulation of A and then
based on these properties show that D's enforcement is effective according to the formal requirements of its policy
A conditional perspective of belief revision
Get PDFBelief Revision is a subarea of Knowledge Representation and Reasoning (KRR) that investigates how to rationally revise an intelligent agent's beliefs in response to new information. There are several approaches to belief revision, but one well-known approach is the AGM model, which is rooted in work by Alchourrón, Gärdenfors, and Makinson. This model provides a set of axioms defining desirable properties of belief revision operators, which manipulate the agent's belief set represented as a set of propositional formulas.
A famous extension to the classical AGM framework of Belief Revision is Darwiche and Pearl's approach to iterated belief revision. They uncovered that the key to rational behavior under iteration is adequate preservation of conditional beliefs, i.e., beliefs the agent is willing to accept in light of (hypothetical) new information. Therefore, they introduced belief revision operators modifying the agent's belief state, built from conditional beliefs. Kern-Isberner fully axiomatized a principle of conditional preservation for belief revision, which captures the core of adequate treatment of conditional beliefs during the revision. This powerful axiom provides the necessary conceptual framework for revising belief states with sets of conditionals as input, and it shows that conditional beliefs are subtle but essential for studying the process of belief revision.
This thesis provides a conditional perspective of Belief Revision for different belief revision scenarios. In the first part, we introduce and investigate a notion of locality for belief revision operators on the semantic level. Hence, we exploit the unique features of conditionals, which allow us to set up local cases and revise according to these cases, s.t., the complexity of the revision task is reduced significantly. In the second part, we consider the general setting of belief revision with respect to additional meta-information accompanying the input information. We demonstrate the versatility and flexibility of conditionals as input for belief revision operators by reducing the parameterized input to a conditional one for two well-known parameterized belief revision operators who are similarly motivated but very different in their technical execution. Our results show that considering conditional beliefs as input for belief revision operators provides a gateway to new insights into the dynamics of belief revision
Tightly Integrated Probabilistic Description Logic Programs for Representing Ontology Mappings
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