677 research outputs found
Model Checking Trust-based Multi-Agent Systems
Trust has been the focus of many research projects, both theoretical and practical, in
the recent years, particularly in domains where open multi-agent technologies are applied
(e.g., Internet-based markets, Information retrieval, etc.). The importance of trust in such
domains arises mainly because it provides a social control that regulates the relationships
and interactions among agents. Despite the growing number of various multi-agent applications, they still encounter many challenges in their formal modeling and the verification
of agents’ behaviors. Many formalisms and approaches that facilitate the specifications of
trust in Multi-Agent Systems (MASs) can be found in the literature. However, most of these
approaches focus on the cognitive side of trust where the trusting entity is normally capable
of exhibiting properties about beliefs, desires, and intentions. Hence, the trust is considered
as a belief of an agent (the truster) involving ability and willingness of the trustee to perform some actions for the truster. Nevertheless, in open MASs, entities can join and leave
the interactions at any time. This means MASs will actually provide no guarantee about the
behavior of their agents, which makes the capability of reasoning about trust and checking
the existence of untrusted computations highly desired.
This thesis aims to address the problem of modeling and verifying at design time
trust in MASs by (1) considering a cognitive-independent view of trust where trust ingredients are seen from a non-epistemic angle, (2) introducing a logical language named Trust
Computation Tree Logic (TCTL), which extends CTL with preconditional, conditional, and graded trust operators along with a set of reasoning postulates in order to explore its capabilities, (3) proposing a new accessibility relation which is needed to define the semantics
of the trust modal operators. This accessibility relation is defined so that it captures the
intuition of trust while being easily computable, (4) investigating the most intuitive and
efficient algorithm for computing the trust set by developing, implementing, and experimenting different model checking techniques in order to compare between them in terms of
memory consumption, efficiency, and scalability with regard to the number of considered
agents, (5) evaluating the performance of the model checking techniques by analyzing the
time and space complexity.
The approach has been applied to different application domains to evaluate its computational performance and scalability. The obtained results reveal the effectiveness of the
proposed approach, making it a promising methodology in practice
Coalgebraic Reasoning with Global Assumptions in Arithmetic Modal Logics
We establish a generic upper bound ExpTime for reasoning with global
assumptions (also known as TBoxes) in coalgebraic modal logics. Unlike earlier
results of this kind, our bound does not require a tractable set of tableau
rules for the instance logics, so that the result applies to wider classes of
logics. Examples are Presburger modal logic, which extends graded modal logic
with linear inequalities over numbers of successors, and probabilistic modal
logic with polynomial inequalities over probabilities. We establish the
theoretical upper bound using a type elimination algorithm. We also provide a
global caching algorithm that potentially avoids building the entire
exponential-sized space of candidate states, and thus offers a basis for
practical reasoning. This algorithm still involves frequent fixpoint
computations; we show how these can be handled efficiently in a concrete
algorithm modelled on Liu and Smolka's linear-time fixpoint algorithm. Finally,
we show that the upper complexity bound is preserved under adding nominals to
the logic, i.e. in coalgebraic hybrid logic.Comment: Extended version of conference paper in FCT 201
Achieving while maintaining:A logic of knowing how with intermediate constraints
In this paper, we propose a ternary knowing how operator to express that the
agent knows how to achieve given while maintaining
in-between. It generalizes the logic of goal-directed knowing how proposed by
Yanjing Wang 2015 'A logic of knowing how'. We give a sound and complete
axiomatization of this logic.Comment: appear in Proceedings of ICLA 201
Efficient treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis
The main goals of this thesis are the development of a computationally efficient framework for stochastic treatment of various important uncertainties in probabilistic seismic hazard and risk assessment, its application to a newly created seismic risk model of Indonesia, and the analysis and quantification of the impact of these uncertainties on the distribution of estimated seismic losses for a large number of synthetic portfolios modeled after real-world counterparts.
The treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis has already been identified as an area that could benefit from increased research attention.
Furthermore, it has become evident that the lack of research considering the development and application of suitable sampling schemes to increase the computational efficiency of the stochastic simulation represents a bottleneck for applications where model runtime is an important factor.
In this research study, the development and state of the art of probabilistic seismic hazard and risk analysis is first reviewed and opportunities for improved treatment of uncertainties are identified.
A newly developed framework for the stochastic treatment of portfolio location uncertainty as well as ground motion and damage uncertainty is presented.
The framework is then optimized with respect to computational efficiency.
Amongst other techniques, a novel variance reduction scheme for portfolio location uncertainty is developed.
Furthermore, in this thesis, some well-known variance reduction schemes such as Quasi Monte Carlo, Latin Hypercube Sampling and MISER (locally adaptive recursive stratified sampling) are applied for the first time to seismic hazard and risk assessment.
The effectiveness and applicability of all used schemes is analyzed.
Several chapters of this monograph describe the theory, implementation and some exemplary applications of the framework.
To conduct these exemplary applications, a seismic hazard model for Indonesia was developed and used for the analysis and quantification of loss uncertainty for a large collection of synthetic portfolios.
As part of this work, the new framework was integrated into a probabilistic seismic hazard and risk assessment software suite developed and used by Munich Reinsurance Group.
Furthermore, those parts of the framework that deal with location and damage uncertainties are also used by the flood and storm natural catastrophe model development groups at Munich Reinsurance for their risk models
Exact Non-Parametric Bayesian Inference on Infinite Trees
Given i.i.d. data from an unknown distribution, we consider the problem of
predicting future items. An adaptive way to estimate the probability density is
to recursively subdivide the domain to an appropriate data-dependent
granularity. A Bayesian would assign a data-independent prior probability to
"subdivide", which leads to a prior over infinite(ly many) trees. We derive an
exact, fast, and simple inference algorithm for such a prior, for the data
evidence, the predictive distribution, the effective model dimension, moments,
and other quantities. We prove asymptotic convergence and consistency results,
and illustrate the behavior of our model on some prototypical functions.Comment: 32 LaTeX pages, 9 figures, 5 theorems, 1 algorith
Efficient treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis
The main goals of this thesis are the development of a computationally efficient framework for stochastic treatment of various important uncertainties in probabilistic seismic hazard and risk assessment, its application to a newly created seismic risk model of Indonesia, and the analysis and quantification of the impact of these uncertainties on the distribution of estimated seismic losses for a large number of synthetic portfolios modeled after real-world counterparts.
The treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis has already been identified as an area that could benefit from increased research attention.
Furthermore, it has become evident that the lack of research considering the development and application of suitable sampling schemes to increase the computational efficiency of the stochastic simulation represents a bottleneck for applications where model runtime is an important factor.
In this research study, the development and state of the art of probabilistic seismic hazard and risk analysis is first reviewed and opportunities for improved treatment of uncertainties are identified.
A newly developed framework for the stochastic treatment of portfolio location uncertainty as well as ground motion and damage uncertainty is presented.
The framework is then optimized with respect to computational efficiency.
Amongst other techniques, a novel variance reduction scheme for portfolio location uncertainty is developed.
Furthermore, in this thesis, some well-known variance reduction schemes such as Quasi Monte Carlo, Latin Hypercube Sampling and MISER (locally adaptive recursive stratified sampling) are applied for the first time to seismic hazard and risk assessment.
The effectiveness and applicability of all used schemes is analyzed.
Several chapters of this monograph describe the theory, implementation and some exemplary applications of the framework.
To conduct these exemplary applications, a seismic hazard model for Indonesia was developed and used for the analysis and quantification of loss uncertainty for a large collection of synthetic portfolios.
As part of this work, the new framework was integrated into a probabilistic seismic hazard and risk assessment software suite developed and used by Munich Reinsurance Group.
Furthermore, those parts of the framework that deal with location and damage uncertainties are also used by the flood and storm natural catastrophe model development groups at Munich Reinsurance for their risk models
Online Handbook of Argumentation for AI: Volume 1
This volume contains revised versions of the papers selected for the first
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and
Stefan Sarkadi and Andreas Xydi
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