757 research outputs found
Theoretical and Computational Basis for CATNETS - Annual Report Year 3
In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined. --Grid Computing
Theoretical and Computational Basis for CATNETS - Annual Report Year 2
In this work the self-organising potential of the CATNETS allocation mechanism is described to provide a more comprehensive view on the research done in this project. The formal description of either the centralised and decentralised approach is presented. Furthermore the agents' bidding model is described and a comprehensive overview on how the catallactic mechanism is incorporated into the middleware and simulator environments is given. --Decentralized Market Mechanisms,Centralized Market Mechanisms,Catallaxy,Market Engineering,Simulator Integration,Prototype Integration
Theoretical and computational basis for CATNETS - annual report year 3
In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined
Vehicle level health assessment through integrated operational scalable prognostic reasoners
Today’s aircraft are very complex in design and need constant monitoring of the
systems to establish the overall health status. Integrated Vehicle Health
Management (IVHM) is a major component in a new future asset management
paradigm where a conscious effort is made to shift asset maintenance from a
scheduled based approach to a more proactive and predictive approach. Its goal is
to maximize asset operational availability while minimising downtime and the
logistics footprint through monitoring deterioration of component conditions.
IVHM involves data processing which comprehensively consists of capturing data
related to assets, monitoring parameters, assessing current or future health
conditions through prognostics and diagnostics engine and providing
recommended maintenance actions.
The data driven prognostics methods usually use a large amount of data to learn
the degradation pattern (nominal model) and predict the future health. Usually
the data which is run-to-failure used are accelerated data produced in lab
environments, which is hardly the case in real life. Therefore, the nominal model
is far from the present condition of the vehicle, hence the predictions will not be
very accurate. The prediction model will try to follow the nominal models which
mean more errors in the prediction, this is a major drawback of the data driven
techniques.
This research primarily presents the two novel techniques of adaptive data driven
prognostics to capture the vehicle operational scalability degradation. Secondary
the degradation information has been used as a Health index and in the Vehicle
Level Reasoning System (VLRS). Novel VLRS are also presented in this research
study. The research described here proposes a condition adaptive prognostics
reasoning along with VLRS
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding
Logical reasoning remains a pivotal component within the realm of artificial
intelligence. The recent evolution of large language models (LLMs) has marked
significant progress in this domain. The adoption of strategies like
chain-of-thought (CoT) has enhanced the performance of LLMs across diverse
reasoning tasks. Nonetheless, logical reasoning that involves proof planning,
specifically those that necessitate the validation of explanation accuracy,
continues to present stumbling blocks. In this study, we first evaluate the
efficacy of LLMs with advanced CoT strategies concerning such tasks. Our
analysis reveals that LLMs still struggle to navigate complex reasoning chains,
which demand the meticulous linkage of premises to derive a cogent conclusion.
To address this issue, we finetune a smaller-scale language model, equipping it
to decompose proof objectives into more manageable subgoals. We also introduce
contrastive decoding to stepwise proof generation, making use of negative
reasoning paths to strengthen the model's capacity for logical deduction.
Experiments on EntailmentBank underscore the success of our method in
augmenting the proof planning abilities of language models
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A unified framework for resource-bounded autonomous agents interacting with unknown environments
The aim of this thesis is to present a mathematical framework for conceptualizing and constructing adaptive autonomous systems under resource constraints. The first part of this thesis contains a concise presentation of the foundations of classical agency: namely the formalizations of decision making and learning. Decision making includes: (a) subjective expected utility (SEU) theory, the framework of decision making under uncertainty; (b) the maximum SEU principle to choose the optimal solution; and (c) its application to the design of autonomous systems, culminating in the Bellman optimality equations. Learning includes: (a) Bayesian probability theory, the theory for reasoning under uncertainty that extends logic; and (b) Bayes-Optimal agents, the application of Bayesian probability theory to the design of optimal adaptive agents. Then, two major problems of the maximum SEU principle are highlighted: (a) the prohibitive computational costs and (b) the need for the causal precedence of the choice of the policy. The second part of this thesis tackles the two aforementioned problems. First, an information-theoretic notion of resources in autonomous systems is established. Second, a framework for resource-bounded agency is introduced. This includes: (a) a maximum bounded SEU principle that is derived from a set of axioms of utility; (b) an axiomatic model of probabilistic causality, which is applied for the formalization of autonomous systems having uncertainty over their policy and environment; and (c) the Bayesian control rule, which is derived from the maximum bounded SEU principle and the model of causality, implementing a stochastic adaptive control law that deals with the case where autonomous agents are uncertain about their policy and environment
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