Results for “reasoning” — Page 2

NASA NTRS1990

In defense of compilation: A response to Davis' form and content in model-based reasoning

In a recent paper entitled 'Form and Content in Model Based Reasoning', Randy Davis argues that model based reasoning research aimed at compiling task specific rules from underlying device models is mislabeled, misguided, and diversionary. Some of Davis' claims are examined and his basic conclusions are challenged about the value of compilation research to the model based reasoning community. In particular, Davis' claim is refuted that model based reasoning is exempt from the efficiency benefits provided by knowledge compilation techniques. In addition, several misconceptions are clarified about the role of representational form in compilation. It is concluded that techniques have the potential to make a substantial contribution to solving tractability problems in model based reasoning.

Keller, Richard↗

NASA NTRS1993

Irrelevance Reasoning in Knowledge Based Systems

This dissertation considers the problem of reasoning about irrelevance of knowledge in a principled and efficient manner. Specifically, it is concerned with two key problems: (1) developing algorithms for automatically deciding what parts of a knowledge base are irrelevant to a query and (2) the utility of relevance reasoning. The dissertation describes a novel tool, the query-tree, for reasoning about irrelevance. Based on the query-tree, we develop several algorithms for deciding what formulas are irrelevant to a query. Our general framework sheds new light on the problem of detecting independence of queries from updates. We present new results that significantly extend previous work in this area. The framework also provides a setting in which to investigate the connection between the notion of irrelevance and the creation of abstractions. We propose a new approach to research on reasoning with abstractions, in which we investigate the properties of an abstraction by considering the irrelevance claims on which it is based. We demonstrate the potential of the approach for the cases of abstraction of predicates and projection of predicate arguments. Finally, we describe an application of relevance reasoning to the domain of modeling physical devices.

INFERENCE↗

NASA NTRS1999

A Framework for Dynamic Constraint Reasoning Using Procedural Constraints

Many complex real-world decision and control problems contain an underlying constraint reasoning problem. This is particularly evident in a recently developed approach to planning, where almost all planning decisions are represented by constrained variables. This translates a significant part of the planning problem into a constraint network whose consistency determines the validity of the plan candidate. Since higher-level choices about control actions can add or remove variables and constraints, the underlying constraint network is invariably highly dynamic. Arbitrary domain-dependent constraints may be added to the constraint network and the constraint reasoning mechanism must be able to handle such constraints effectively. Additionally, real problems often require handling constraints over continuous variables. These requirements present a number of significant challenges for a constraint reasoning mechanism. In this paper, we introduce a general framework for handling dynamic constraint networks with real-valued variables, by using procedures to represent and effectively reason about general constraints. The framework is based on a sound theoretical foundation, and can be proven to be sound and complete under well-defined conditions. Furthermore, the framework provides hybrid reasoning capabilities, as alternative solution methods like mathematical programming can be incorporated into the framework, in the form of procedures.

Jonsson, Ari K.↗

DOE OSTI2026

Compositional Reasoning for Hierarchical State Machines

Harel statecharts and its derivatives are popular graphical languages for specifying discrete control systems via hierarchical state machines. Separately, there has been a long line of work on specifying concurrent systems with process calculi which come equipped with an algebraic theory, the ability reason compositionally about various temporal properties, and strong type systems. While these two approaches to modeling systems are tantalizingly similar, the integrated reasoning principles that exist for process calculi have not been demonstrated in hierarchical state machines. A key issue is that operational theories for process calculi do not behave like control systems, and thus, there is virtually no tool support for modeling control systems with such languages. For a control system designer, bringing the integrated, more scalable reasoning from the process calculi to state-machine languages would enable the specification of more complex systems and a more modular systems development process. Our insight is that we can recover many important results from the process calculi in hierarchical state machines with local scope. We employ a structural operational semantics, which is ubiquitous in process and 𝜆-calculi but uncommon in hierarchical statemachine formalizations, to enable inductive reasoning about behavior. Taking inspiration from the structure of process calculi metatheories, we define a calculus of refinement and equivalence that we prove sound with respect to local notion of (bi)simulation. Furthermore, we prove that the calculus preserves the behavioral properties of reactivity, observational determinism, traces, and linear temporal properties. Our results are mechanized in the Rocq proof assistant.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Data & Reasoning Fabric Minimum Viable Product

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of air mobility by providing all data and reasoning where they are needed. The DRF provides a marketplace based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the complex and dense airspace operations required to achieve advanced air mobility goals. The DRF marketplace is decentralized and will not be owned by any single party. To deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of DRF and associated standards, DRF activities will collaboratively test these technologies, open standards and architectures, and an integrated framework with end-users. The DRF activity is developing a Minimum Viable Product (MVP) called the DRF Accelerator which will enable the rapid deployment of candidate data and reasoning services to test and evaluate the DRF core system functionality. The DRF Project is engaging with government and industry end-users and stakeholders, to: (1) assess the technical feasibility of the DRF Accelerator; (2) assess the likelihood of adoption, through shared test and evaluation; and (3) identify what data and services can be shared across DRF.

DRF↗

NASA NTRS1987

Case-based reasoning for space applications: Utilization of prior experience in knowledge-based systems

The goal is to explain Case-Based Reasoning as a vehicle to establish knowledge-based systems based on experimental reasoning for possible space applications. This goal will be accomplished through an examination of reasoning based on prior experience in a sample domain, and also through a presentation of proposed space applications which could utilize Case-Based Reasoning techniques.

King, James A.↗

NASA NTRS1988

Expertise and reasoning with possibility: An explanation of modal logic and expert systems

Recently systems of modal reasoning have been brought to the foreground of artificial intelligence studies. The intuitive idea of research efforts in this area is that in addition to the actual world in which sentences have certain truth values there are other worlds in which those sentences have different truth values. Such alternative worlds can be considered as possible worlds, and an agent may or may not have access to some or all of them. This approach to reasoning can be valuable in extending the expert system paradigm. Using the scheme of reasoning proposed by Toulmin, Reike and Janick and the modal system T, a scheme is proposed for expert reasoning that mitigates some of the criticisms raised by Schank and Nickerson.

Rochowiak, Daniel↗

NASA NTRS1989

A situated reasoning architecture for space-based repair and replace tasks

Space-based robots need low level control for collision detection and avoidance, short-term load management, fine-grained motion, and other physical tasks. In addition, higher level control is required to focus strategic decision making as missions are assigned and carried out. Reasoning and control must be responsive to ongoing changes in the environment. Research aimed at bridging the gap between high level artificial intelligence (AI) planning techniques and task-level robot programming for telerobotic systems is described. Situated reasoning is incorporated into AI and Robotics systems in order to coordinate a robot's activity within its environment. An integrated system under development in a component maintenance domain is described. It is geared towards replacing worn and/or failed Orbital Replacement Units (ORUs) designed for use aboard NASA's Space Station Freedom based on the collection of components available at a given time. High level control reasons in component space in order to maximize the number operational component-cells over time, while the task-level controls sensors and effectors, detects collisions, and carries out pick and place tasks in physical space. Situated reasoning is used throughout the system to cope with component failures, imperfect information, and unexpected events.

Bloom, Ben↗

NASA NTRS1990

Modelling default and likelihood reasoning as probabilistic

A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'Likely' and 'by default' are in fact treated as duals in the same sense as 'possibility' and 'necessity'. To model these four forms probabilistically, a logic QDP and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.

Buntine, Wray↗

NASA NTRS1994

Open architectures for formal reasoning and deductive technologies for software development

The objective of this project is to develop an open architecture for formal reasoning systems. One goal is to provide a framework with a clear semantic basis for specification and instantiation of generic components; construction of complex systems by interconnecting components; and for making incremental improvements and tailoring to specific applications. Another goal is to develop methods for specifying component interfaces and interactions to facilitate use of existing and newly built systems as 'off the shelf' components, thus helping bridge the gap between producers and consumers of reasoning systems. In this report we summarize results in several areas: our data base of reasoning systems; a theory of binding structures; a theory of components of open systems; a framework for specifying components of open reasoning system; and an analysis of the integration of rewriting and linear arithmetic modules in Boyer-Moore using the above framework.

Mccarthy, John↗

NASA NTRS2003

Characterization of Model-Based Reasoning Strategies for Use in IVHM Architectures

Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.

Poll, Scott↗

NASA NTRS2004

An Automated Approach to Reasoning Under Multiple Perspectives

This is the final report with emphasis on research during the last term. The context for the research has been the development of an automated reasoning technology for use in SMS (symbolic Manipulation System), a system used to build and query knowledge bases (KBs) using a special knowledge representation language SL (Symbolic Language). SMS interpreters assertive SL input and enters the results as components of its universe. The system operates in two basic models: 1) constructive mode (for building KBs); and 2) query/search mode (for querying KBs). Query satisfaction consists of matching query components with KB components. The system allows "penumbral matches," that is, matches that do not exactly meet the specifications of the query, but which are deemed relevant for the conversational context. If the user wants to know whether SMS has information that holds, say, for "any chow," the scope of relevancy might be set so that the system would respond based on a finding that it has information that holds for "most dogs," although this is not exactly what was called for by the query. The response would be qualified accordingly, as would normally be the case in ordinary human conversation. The general goal of the research was to develop an approach by which assertive content could be interpreted from multiple perspectives so that reasoning operations could be successfully conducted over the results. The interpretation of an SL statement such as, "{person believes [captain (asserted (perhaps)) (astronaut saw (comet (bright)))]}," which in English would amount to asserting something to the effect that, "Some person believes that a captain perhaps asserted that an astronaut saw a bright comet," would require the recognition of multiple perspectives, including some that are: a) epistemically-based (focusing on "believes"); b) assertion-based (focusing on "asserted"); c) perception-based (focusing on "saw"); d) adjectivally-based (focusing on "bight"); and e) modally-based (focusing on "perhaps"). Any conclusion reached under a line of reasoning that employs such an assertion or its associated implications should somehow reflect the employed perspectives. The investigators made significant progress in developing an approach that would enable a system to conduct reasoning operations over assertions of this kind while maintaining consistency in its knowledge bases. Significant accomplishments were made in the areas of: 1) integration and inferencing; 2) generation of perspectives, including wholistic ad composite views; and 3) consistency maintenance.

deBessonet, Cary↗

NASA NTRSDate not supplied

A Data & Reasoning Fabric to Enable Advanced Air Mobility

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of advanced air mobility by providing all data and reasoning where they are needed. The DRF marketplace is based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the envisioned highly complex and dense airspace operations. DRF activities will identify, test and - as needed - research and develop critical core technologies, and collaboratively test these technologies, open standards and architectures, and the integrated framework with end-users so as to deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of it and associated standards.

Urban Air Mobility↗

NASA NTRSDate not supplied

Data and Reasoning Fabric (DRF) Phase I & Phase II Report

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of advanced air mobility by providing all data and reasoning where they are needed. The DRF marketplace is based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the envisioned highly complex and dense airspace operations. DRF activities will identify, test and - as needed - research and develop critical core technologies, and collaboratively test these technologies, open standards and architectures, and the integrated framework with end-users so as to deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of it and associated standards.

UAM↗

NASA NTRSDate not supplied

Data & Reasoning Fabric: Minimum Viable Product

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of advanced air mobility by providing all data and reasoning where they are needed. The DRF provides a marketplace based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the complex and dense airspace operations required to achieve advanced air mobility goals. DRF activities will identify, test and - as needed - research and develop critical core technologies. In order to deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of DRF and associated standards, DRF activities will collaboratively test these technologies, open standards and architectures, and an integrated framework with end users.

UAM↗

NASA NTRS2021

A Data & Reasoning Fabric to Enable Advanced Air Mobility

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of advanced air mobility by providing all data and reasoning where they are needed. The DRF marketplace is based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the envisioned highly complex and dense airspace operations. DRF activities will identify, test and - as needed - research and develop critical core technologies, and collaboratively test these technologies, open standards and architectures, and the integrated framework with end-users so as to deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of it and associated standards.

Urban Air Mobility↗

NASA NTRSDate not supplied

Data & Reasoning Fabric Minimum Viable Product

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of air mobility by providing all data and reasoning where they are needed. The DRF provides a marketplace based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the complex and dense airspace operations required to achieve advanced air mobility goals. The DRF marketplace is decentralized and will not be owned by any single party. To deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of DRF and associated standards, DRF activities will collaboratively test these technologies, open standards and architectures, and an integrated framework with end-users.

DRF↗

NASA NTRSDate not supplied

Data and Reasoning Fabric (DRF)

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of air mobility by providing all data and reasoning where they are needed. The DRF provides a marketplace based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the complex and dense airspace operations required to achieve advanced air mobility goals. The DRF marketplace is decentralized and will not be owned by any single party. To deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of DRF and associated standards, DRF activities will collaboratively test these technologies, open standards and architectures, and an integrated framework with end-users.

Aeronautics↗