SEARCH · Search NASA

Results for “reasoning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

All sources NASA NTRS DOE OSTI

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. Presentation has a run time of 16:58 and is an mp4 attachment included in documents.

Aeronautics↗

NASA NTRS2023

Data and Reasoning Fabric (DRF)

Aeronautics↗

NASA NTRSDate not supplied

Data and Reasoning Fabric (DRF)

Aeronautics↗

DOE OSTI2026

The ballad of LLM agents: philosophical reasoning for chemistry

Large language models (LLMs) show remarkable potential for scientific reasoning but often produce unreliable or scientifically unactionable outputs when faced with multi-step logic, domain grounding, and interpretability challenges, especially in complex fields like chemistry and materials science. Here, we introduce a framework of philosophical reasoning agents, inspired by canonical thinkers such as Socrates, Descartes, Kant, and Hume, to guide LLM behavior via structured prompt engineering. These agents embody distinct reasoning paradigms (dialectical inquiry, deductive logic, rule-based judgment, and empirical validation) and are evaluated across multiple chemistry subdomains, physical, analytical, general, inorganic, and organic chemistry, using the ChemBench benchmark. Our agentic prompting approach yields substantial accuracy gains on open-ended numerical chemistry questions, with gains of +11.5 percentage points for GPT-4o with Hume, +4.5 percentage points for GPT-5 with Kant, and +21.8 percentage points for GPT-5.1 with Socrates at the strict 1% error threshold, relative to the corresponding base models. Beyond accuracy, we observe benchmark-level model–agent performance patterns, suggesting that different prompting styles interact differently with each base model. These findings demonstrate that embedding philosophy-of-science principles into multi-agent frameworks can improve and produce interpretable, adaptive, and domain-aligned scientific LLMs.

Harb, Hassan [Argonne National Laboratory (ANL), A↗

NASA NTRS1985

Reasoning about procedural knowledge

A crucial aspect of automated reasoning about space operations is that knowledge of the problem domain is often procedural in nature - that is, the knowledge is often in the form of sequences of actions or procedures for achieving given goals or reacting to certain situations. In this paper a system is described that explicitly represents and reasons about procedural knowledge. The knowledge representation used is sufficiently rich to describe the effects of arbitrary sequences of tests and actions, and the inference mechanism provides a means for directly using this knowledge to reach desired operational goals. Furthermore, the representation has a declarative semantics that provides for incremental changes to the system, rich explanatory capabilities, and verifiability. The approach also provides a mechanism for reasoning about the use of this knowledge, thus enabling the system to choose effectively between alternative courses of action.

Georgeff, M. P.↗

NASA NTRS1988

An approach to combining heuristic and qualitative reasoning in an expert system

An approach to combining the heuristic reasoning from shallow knowledge and the qualitative reasoning from deep knowledge is described. The shallow knowledge is represented in production rules and under the direct control of the inference engine. The deep knowledge is represented in frames, which may be put in a relational DataBase Management System. This approach takes advantage of both reasoning schemes and results in improved efficiency as well as expanded problem solving ability.

Jiang, Wei-Si↗

NASA NTRS1988

Knowledge acquisition for case-based reasoning systems

Case-based reasoning (CBR) is a simple idea: solve new problems by adapting old solutions to similar problems. The CBR approach offers several potential advantages over rule-based reasoning: rules are not combined blindly in a search for solutions, solutions can be explained in terms of concrete examples, and performance can improve automatically as new problems are solved and added to the case library. Moving CBR for the university research environment to the real world requires smooth interfaces for getting knowledge from experts. Described are the basic elements of an interface for acquiring three basic bodies of knowledge that any case-based reasoner requires: the case library of problems and their solutions, the analysis rules that flesh out input problem specifications so that relevant cases can be retrieved, and the adaptation rules that adjust old solutions to fit new problems.

Riesbeck, Christopher K.↗

NASA NTRS1987

Reasoning and planning in dynamic domains: An experiment with a mobile robot

Progress made toward having an autonomous mobile robot reason and plan complex tasks in real-world environments is described. To cope with the dynamic and uncertain nature of the world, researchers use a highly reactive system to which is attributed attitudes of belief, desire, and intention. Because these attitudes are explicitly represented, they can be manipulated and reasoned about, resulting in complex goal-directed and reflective behaviors. Unlike most planning systems, the plans or intentions formed by the system need only be partly elaborated before it decides to act. This allows the system to avoid overly strong expectations about the environment, overly constrained plans of action, and other forms of over-commitment common to previous planners. In addition, the system is continuously reactive and has the ability to change its goals and intentions as situations warrant. Thus, while the system architecture allows for reasoning about means and ends in much the same way as traditional planners, it also posseses the reactivity required for survival in complex real-world domains. The system was tested using SRI's autonomous robot (Flakey) in a scenario involving navigation and the performance of an emergency task in a space station scenario.

Georgeff, M. P.↗

NASA NTRS1987

Prediction and causal reasoning in planning

Nonlinear planners are often touted as having an efficiency advantage over linear planners. The reason usually given is that nonlinear planners, unlike their linear counterparts, are not forced to make arbitrary commitments to the order in which actions are to be performed. This ability to delay commitment enables nonlinear planners to solve certain problems with far less effort than would be required of linear planners. Here, it is argued that this advantage is bought with a significant reduction in the ability of a nonlinear planner to accurately predict the consequences of actions. Unfortunately, the general problem of predicting the consequences of a partially ordered set of actions is intractable. In gaining the predictive power of linear planners, nonlinear planners sacrifice their efficiency advantage. There are, however, other advantages to nonlinear planning (e.g., the ability to reason about partial orders and incomplete information) that make it well worth the effort needed to extend nonlinear methods. A framework is supplied for causal inference that supports reasoning about partially ordered events and actions whose effects depend upon the context in which they are executed. As an alternative to a complete but potentially exponential-time algorithm, researchers provide a provably sound polynomial-time algorithm for predicting the consequences of partially ordered events.

Dean, T.↗

NASA NTRS1988

Approximate spatial reasoning

A model for approximate spatial reasoning using fuzzy logic to represent the uncertainty in the environment is presented. Algorithms are developed which can be used to reason about spatial information expressed in the form of approximate linguistic descriptions similar to the kind of spatial information processed by humans. Particular attention is given to static spatial reasoning.

Dutta, Soumitra↗

NASA NTRS1990

Analyzing spacecraft configurations through specialization and default reasoning

For an intelligent system to describe a real-world situation using as few statements as possible, it is necessary to make inferences based on observed data and to incorporate general knowledge of the reasoning domain into the description. These reasoning processes must reduce several levels of specific descriptions into only those few that most precisely describe the situation. Moreover, the system must be able to generate descriptions in the absence of data, as instructed by certain rules of inference. The deductions applied by the system, then, generate a high-level description from the low-level evidence provided by the real and default data sources. An implementation of these ideas in a real-world situation is described. The application concerns evaluation of Space Shuttle electromechanical system configurations by console operators in the Mission Control Center. A production system provides the reasoning mechanism through which the default assignments and specializations occur. Examples are provided within this domain for each type of inference, and the suitability is discussed of each toward achieving the goal of describing a situation in the fewest statements possible. Finally, several enhancements are suggested that will further increase the intelligence of similar spacecraft monitoring applications.

Barry, Matthew R.↗

NASA NTRS1990

Monitoring and control of spacecraft systems using procedural reasoning

Research concerned with automating the monitoring and control of spacecraft systems is discussed. In particular, the application of SRI's Procedural Reasoning System (PRS) to the handling of malfunctions in the Reaction Control System (RCS) of NASA's Space Shuttle is examined. Unlike traditional monitoring and control systems, PRS is able to reason about and perform complex tasks in a very flexible and robust manner, somewhat in the manner of a human assistant. Using various RCS malfunctions as examples (including sensor faults, leaking components, multiple alarms, and regulator and jet failures), it is shown how PRS manages to combine both goal-directed reasoning and the ability to react rapidly to unanticipated changes in its environment. In conclusion, some important issues in the design of PRS are reviewed and future enhancements are indicated.

Georgeff, Michael P.↗

NASA NTRS1990

ATS displays: A reasoning visualization tool for expert systems

Reasoning visualization is a useful tool that can help users better understand the inherently non-sequential logic of an expert system. While this is desirable in most all expert system applications, it is especially so for such critical systems as those destined for space-based operations. A hierarchical view of the expert system reasoning process and some characteristics of these various levels is presented. Also presented are Abstract Time Slice (ATS) displays, a tool to visualize the plethora of interrelated information available at the host inferencing language level of reasoning. The usefulness of this tool is illustrated with some examples from a prototype potable water expert system for possible use aboard Space Station Freedom.

Selig, William John↗

NASA NTRS1989

Overcoming limitations of model-based diagnostic reasoning systems

The development of a model-based diagnostic system to overcome the limitations of model-based reasoning systems is discussed. It is noted that model-based reasoning techniques can be used to analyze the failure behavior and diagnosability of system and circuit designs as part of the system process itself. One goal of current research is the development of a diagnostic algorithm which can reason efficiently about large numbers of diagnostic suspects and can handle both combinational and sequential circuits. A second goal is to address the model-creation problem by developing an approach for using design models to construct the GMODS model in an automated fashion.

Holtzblatt, Lester J.↗

NASA NTRS1989

Towards reasoning visualization in expert systems

Preliminary results are presented of ongoing research to develop visualization paradigms for expert systems reasoning processes. The results are based in part on a prototype implementation that is being developed to visualize the reasoning processes of a rule-based forward chaining expert system. The research is based on the premise that the presentation of information at the highest applicable conceptual level will enhance the assimilation of that information. Hierarchical levels in both the syntactic and semantic levels of reasoning in expert systems are described in detail.

Selig, William John↗

NASA NTRS1991

Reasoning about real-time systems with temporal interval logic constraints on multi-state automata

Models of real-time systems using a single paradigm often turn out to be inadequate, whether the paradigm is based on states, rules, event sequences, or logic. A model-based approach to reasoning about real-time systems is presented in which a temporal interval logic called TIL is employed to define constraints on a new type of high level automata. The combination, called hierarchical multi-state (HMS) machines, can be used to model formally a real-time system, a dynamic set of requirements, the environment, heuristic knowledge about planning-related problem solving, and the computational states of the reasoning mechanism. In this framework, mathematical techniques were developed for: (1) proving the correctness of a representation; (2) planning of concurrent tasks to achieve goals; and (3) scheduling of plans to satisfy complex temporal constraints. HMS machines allow reasoning about a real-time system from a model of how truth arises instead of merely depending of what is true in a system.

Gabrielian, Armen↗

NASA NTRS1990

Real-time reasoning - The monitoring and control of spacecraft systems

Research concerned with automating the monitoring and control of spacecraft systems is discussed. In particular, the application the Procedural Reasoning System (PRS) to the handling of malfunctions in the Reaction Control System (RCS) of NASAs space shuttle is studied. Unlike traditional monitoring and control systems, PRS is able to reason about and perform complex tasks in a very flexible and robust manner, somewhat in the manner of a human assistant. Using various RCS malfunctions as examples (including sensor faults, leaking components, multiple alarms, and regulator and jet failures), it is shown how PRS manages to combine both goal-directed reasoning and the ability to react rapidly to unanticipated changes in its environment. Some important issues in the design of PRS are reviewed, and future enhancements are indicated.

Georgeff, Michael P.↗

NASA NTRS1991

ECLIPS: An extended CLIPS for backward chaining and goal-directed reasoning

Realistic production systems require an integrated combination of forward and backward reasoning to reflect appropriately the processes of natural human expert reasoning. A control mechanism that consists solely of forward reasoning is not an effective way to promptly focus the system's attention as calculation proceeds. Often, expert system programmers will attempt to compensate for this lack by using data to enforce the desired goal directed control structure. This approach is inherently flawed in that it is attempting to use data to fulfill the role of control. This paper will describe our implementation of backward chaining in C Language Integrated Production System (CLIPS), and show how this has shortened and simplified various CLIPS programs. This work was done at the Aerospace Corporation, and has general applicability.

Homeier, Peter V.↗