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Results for “Reasoning”

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At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

NASA NTRS1991

On the integration of reinforcement learning and approximate reasoning for control

The author discusses the importance of strengthening the knowledge representation characteristic of reinforcement learning techniques using methods such as approximate reasoning. The ARIC (approximate reasoning-based intelligent control) architecture is an example of such a hybrid approach in which the fuzzy control rules are modified (fine-tuned) using reinforcement learning. ARIC also demonstrates that it is possible to start with an approximately correct control knowledge base and learn to refine this knowledge through further experience. On the other hand, techniques such as the TD (temporal difference) algorithm and Q-learning establish stronger theoretical foundations for their use in adaptive control and also in stability analysis of hybrid reinforcement learning and approximate reasoning-based controllers.

Berenji, Hamid R.↗

NASA NTRS1994

Filtering as a reasoning-control strategy: An experimental assessment

In dynamic environments, optimal deliberation about what actions to perform is impossible. Instead, it is sometimes necessary to trade potential decision quality for decision timeliness. One approach to achieving this trade-off is to endow intelligent agents with meta-level strategies that provide them guidance about when to reason (and what to reason about) and when to act. We describe our investigations of a particular meta-level reasoning strategy, filtering, in which an agent commits to the goals it has already adopted, and then filters from consideration new options that would conflict with the successful completion of existing goals. To investigate the utility of filtering, a series of experiments was conducted using the Tileworld testbed. Previous experiments conducted by Kinny and Georgeff used an earlier version of the Tileworld to demonstrate the feasibility of filtering. Results are presented that replicate and extend those of Kinny and Georgeff and demonstrate some significant environmental influences on the value of filtering.

Pollack, Martha E.↗

NASA NTRS1994

UM-PRS: An implementation of the procedural reasoning system for multirobot applications

The Procedural Reasoning System (PRS) is used in applications where predetermined situations might arise. The UM-PRS provides a reasoning system that represents robotic applications even in unpredictable domains, such as the robotic reconnaissance task domain outlined here. UM-PRS incorporates a changing context, rather than relying solely on a prearranged plan. The UM-PRS here provides representation important in the reasoning and interface between a mission plan and the executable map of an outdoor vehicle that changes its behavior based on what it comes in contact with in its environment. PRS is thus used in the dynamic control of such a vehicle, providing the basis for coordinating the joint task of multiple robotic vehicles by the their individual observations and representation.

Lee, Jaeho↗

NASA NTRS1994

Reflexive reasoning for distributed real-time systems

This paper discusses the implementation and use of reflexive reasoning in real-time, distributed knowledge-based applications. Recently there has been a great deal of interest in agent-oriented systems. Implementing such systems implies a mechanism for sharing knowledge, goals and other state information among the agents. Our techniques facilitate an agent examining both state information about other agents and the parameters of the knowledge-based system shell implementing its reasoning algorithms. The shell implementing the reasoning is the Distributed Artificial Intelligence Toolkit, which is a derivative of CLIPS.

Goldstein, David↗

NASA NTRS2001

Temporal Constraint Reasoning With Preferences

A number of reasoning problems involving the manipulation of temporal information can naturally be viewed as implicitly inducing an ordering of potential local decisions involving time (specifically, associated with durations or orderings of events) on the basis of preferences. For example. a pair of events might be constrained to occur in a certain order, and, in addition. it might be preferable that the delay between them be as large, or as small, as possible. This paper explores problems in which a set of temporal constraints is specified, where each constraint is associated with preference criteria for making local decisions about the events involved in the constraint, and a reasoner must infer a complete solution to the problem such that, to the extent possible, these local preferences are met in the best way. A constraint framework for reasoning about time is generalized to allow for preferences over event distances and durations, and we study the complexity of solving problems in the resulting formalism. It is shown that while in general such problems are NP-hard, some restrictions on the shape of the preference functions, and on the structure of the preference set, can be enforced to achieve tractability. In these cases, a simple generalization of a single-source shortest path algorithm can be used to compute a globally preferred solution in polynomial time.

Khatib, Lina↗

NASA NTRS2010

Automated Assume-Guarantee Reasoning for Omega-Regular Systems and Specifications

We develop a learning-based automated Assume-Guarantee (AG) reasoning framework for verifying omega-regular properties of concurrent systems. We study the applicability of non-circular (AGNC) and circular (AG-C) AG proof rules in the context of systems with infinite behaviors. In particular, we show that AG-NC is incomplete when assumptions are restricted to strictly infinite behaviors, while AG-C remains complete. We present a general formalization, called LAG, of the learning based automated AG paradigm. We show how existing approaches for automated AG reasoning are special instances of LAG.We develop two learning algorithms for a class of systems, called infinite regular systems, that combine finite and infinite behaviors. We show that for infinity-regular systems, both AG-NC and AG-C are sound and complete. Finally, we show how to instantiate LAG to do automated AG reasoning for infinite regular, and omega-regular, systems using both AG-NC and AG-C as proof rules

Chaki, Sagar↗

NASA NTRS2003

Abstract-Reasoning Software for Coordinating Multiple Agents

A computer program for scheduling the activities of multiple agents that share limited resources has been incorporated into the Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles. In the original intended application, the agents would be multiple spacecraft and/or robotic vehicles engaged in scientific exploration of distant planets. The program could also be used on Earth in such diverse settings as production lines and military maneuvers. This program includes a planning/scheduling subprogram of the iterative repair type that reasons about the activities of multiple agents at abstract levels in order to greatly improve the scheduling of their use of shared resources. The program summarizes the information about the constraints on, and resource requirements of, abstract activities on the basis of the constraints and requirements that pertain to their potential refinements (decomposition into less-abstract and ultimately to primitive activities). The advantage of reasoning about summary information is that time needed to find consistent schedules is exponentially smaller than the time that would be needed for reasoning about the same tasks at the primitive level.

Clement, Bradley↗

DOE OSTI2023

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗

DOE OSTI2020

Panel Session 132: Risk-Informed Approach for Decision Making in WM, D and D and SNF Management: Reasonable Assurance for Safety

Mr. Larry Camper organized a panel of experts to discuss approaches to better make risk-informed decisions in waste management, decommissioning, and the management of Spent Nuclear Fuel (SNF). The audience heard the perspectives from four panelists that addressed issues ranging from the technical basis used to make risk-informed decisions to for developing cleanup criteria and promulgating regulations and safety standards both domestically and abroad. A summary of each of the presentations given by the panelists is provided herein. This WMS BOD featured panel focused on the Risk-Informed Approach for Decision Making in WM, D and D and SNF Management and the Reasonable Assurance for Safety. The panelists addressed and discussed with the audience different approaches used for decision-making, summarizing ongoing probabilistic vs. deterministic approaches, including IAEA graded approach, and discussed policies/approaches to achieve reasonable assurance for safety rather than using absolute assurance. Panelists with presentations: Risk-Informed Decision Making - More than a Motto? (Paul Black); NRC Staff Perspective on Risk-Informed Approach and Reasonable Safety Assurance in D and D and LLW (Rateb (Boby) Abu Eid); Risk-Informed Decision-Making and Illustrative National Academies Studies (Charles Ferguson); IAEA's Revised Safety Guidance on Remediation (Michelle Roberts); NDA Radioactive Waste Strategy - A Risk Informed Approach (James McKinney)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

AI-Guided Reasoning-Based Operator Support System for the Nuclear Power Plant Management

The Decision-making process in the Nuclear Power Plant (NPP) control room faces some challenges: operator incomplete knowledge, insufficient time for responding to the highly dynamic events, and a large number of indicators to monitor. Because of the complexity of the NPP system, it is hard to pre-plan all the failures/mitigative actions. An intelligent operator support system is vital to mitigate these shortcomings. In this paper, an AI declarative approach (Answer Set Programming (ASP)) is employed to represent our knowledge of the nuclear power plant in the form of logic rules. This represented knowledge is structured to form a reasoning-based operator support system. When an incident occurs, this ASP-based reasoning support system is demonstrated to be capable of fault identification (diagnosis), informing the operator of different scenarios and consequences, and generating the control options (decision making).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Alerga: Alert Aggregation and Reasoning in GOOSE Simulation Pipeline

IEC 61850 specifies the Generic Object Oriented Substation Event (GOOSE) protocol as one option for low latency communication of substation-related events. Due to its strict timing requirements, GOOSE lacks any form of encryption or authentication and has only minimal integrity guarantees. These absences render the protocol vulnerable to a variety of communication anomalies, including adversarial action. In particular, an adversary with access to the substation network can launch man in the middle (MITM) attacks. We propose Alerga, a set of tools to allow operators to mitigate some of the risks of the protocol while retaining its strengths. To that end, we have developed first a GOOSE simulation pipeline including data generation, anomaly detection, alert handling, causal reasoning and data visualization components. The simulator is designed to be modular, allowing operators to swap components to better fit their network capabilities. The volume of alert traffic on a substation network threatens operators with alert fatigue. In order to combat this, we secondly present a novel form of alert aggregation and processing, offering operators a condensed view of any threats to the system. Thirdly, to facilitate the handling of these threats, our causal reasoning system traces the alerts back to their most likely cause, generating an initial hypothesis for operators to investigate.

alert aggregation↗

DOE OSTI2024

Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning

Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models helps to trace evidence from an externally provided knowledge base allowing the model predictions to be better interpreted and verified. In this work, we critically evaluate these models in their ability to perform in scientific document reasoning tasks. To this end, we tuned multiple such model variants with science-focused instructions and evaluated them on a scientific document reasoning benchmark for the usefulness of the retrieved document passages. Our findings suggest that models justify predictions in science tasks with fabricated evidence and leveraging scientific corpus as pretraining data does not alleviate the risk of evidence fabrication.

• Artificial intelligence (AI) / machine learning ↗

NASA NTRS2005

Probabilistic Reasoning for Plan Robustness

A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations witnessed by execution. The improvement is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.

autonomous planning↗

NASA NTRS2011

Vehicle-Level Reasoning Systems: Integrating System-Wide Data to Estimate the Instantaneous Health State

At the aircraft level, a Vehicle-Level Reasoning System (VLRS) can be developed to provide aircraft with at least two significant capabilities: improvement of aircraft safety due to enhanced monitoring and reasoning about the aircrafts health state, and also potential cost savings by enabling Condition Based Maintenance (CBM). Along with the benefits of CBM, an important challenge facing aviation safety today is safeguarding against system and component failures and malfunctions. Faults can arise in one or more aircraft subsystem their effects in one system may propagate to other subsystems, and faults may interact.

system-wide data↗

NASA NTRSDate not supplied

Data & Reasoning Fabric

A Data & Reasoning Fabric shall provide a set of secured software infrastructure, tools, protocols, governance and policies to implement, administer, manage and operate data sharing and reasoning services across the entire span of air mobility and other ”smart” edge nodes.

Aeronautics↗

NASA NTRSDate not supplied

Data & Reasoning Fabric Overview

National Airspace↗

DOE OSTI2023

Right band gaps for the right reason at low computational cost with a meta-GGA

In density functional theory, traditional explicit density functionals such as the local density approximation and generalized gradient approximations cannot accurately predict the band gap of solids for a fundamental reason: They lack the exchange-correlation derivative discontinuity. By comparing Kohn-Sham and generalized Kohn-Sham calculations, we here show that the nonempirical meta-generalized-gradient-approximation (meta-GGA) TASK from Aschebrock and Kümmel [Phys. Rev. Res. 1, 033082 (2019)] predicts the right gaps for the right reason, i.e., as a combination of a proper Kohn-Sham gap and a substantial derivative discontinuity contribution. For many materials from small-gap semiconductors to large-gap insulators, the proper band gap is thus obtained. Here we further study a group of metal-halide perovskites for which the band gap is notoriously hard to predict. For these materials, TASK yields band gaps very similar to the nonlocal screened hybrid Heyd-Scuseria-Ernzerhof functional, yet at a fraction of the hybrid functional’s computational cost. We discuss the influence of correlation functionals, and open questions in the comparison of calculated band gaps with experimental ones.

36 MATERIALS SCIENCE↗

DOE OSTI2024

GraphAide: Advanced Graph-Assisted Query and Reasoning System

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Purohit, Sumit [BATTELLE (PACIFIC NW LAB)] (ORCID:↗