Revision of Specification Automata under Quantitative Preferences

We study the problem of revising specifications with preferences for automata based control synthesis problems. In this class of revision problems, the user provides a numerical ranking of the desirability of the subgoals in their specifications. When the specification cannot be satisfied on the system, then our algorithms automatically revise the specification so that the least desirable user goals are removed from the specification. We propose two different versions of the revision problem with preferences. In the first version, the algorithm returns an exact solution while in the second version the algorithm is an approximation algorithm with non-constant approximation ratio. Finally, we demonstrate the scalability of our algorithms and we experimentally study the approximation ratio of the approximation algorithm on random problem instances.Comment: 9 pages, 3 figures, 3 tables, in Proceedings of the IEEE Conference on Robotics and Automation, May 201

Extended LTLvis Motion Planning interface (Extended Technical Report)

This paper introduces an extended version of the Linear Temporal Logic (LTL) graphical interface. It is a sketch based interface built on the Android platform which makes the LTL control interface more straightforward and friendly to nonexpert users. By predefining a set of areas of interest, this interface can quickly and efficiently create plans that satisfy extended plan goals in LTL. The interface can also allow users to customize the paths for this plan by sketching a set of reference trajectories. Given the custom paths by the user, the LTL specification and the environment, the interface generates a plan balancing the customized paths and the LTL specifications. We also show experimental results with the implemented interface.Comment: 8 pages, 15 figures, a technical report for the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016

Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration

This paper introduces a novel approach to detour management in Urban Air Traffic Management (UATM) using knowledge representation and reasoning. It aims to understand the complexities and requirements of UAM detours, enabling a method that quickly identifies safe and efficient routes in a carefully sampled environment. This method implemented in Answer Set Programming uses non-monotonic reasoning and a two-phase conversation between a human manager and the UATM system, considering factors like safety and potential impacts. The robustness and efficacy of the proposed method were validated through several queries from two simulation scenarios, contributing to the symbiosis of human knowledge and advanced AI techniques. The paper provides an introduction, citing relevant studies, problem formulation, solution, discussions, and concluding comments.Comment: 18 pages, 2 figures, accepted to the Advances in Artificial Intelligence and Machine Learning (AAIML) journa

Agent 3, change your route: possible conversation between a human manager and UAM Air Traffic Management (UATM)

This work in progress paper provides an example to show a detouring procedure through knowledge representation and reasoning. When a human manager requests a detouring, this should affect the related agents. Through non-monotonic reasoning process, we verify each step to be proceeded and provide all the successful connections of the reasoning. Following this progress and continuing this idea development, we expect that this simulated scenario can be a guideline to build the traffic management system in real. After a brief introduction including related works, we provide our problem formulation, primary work, discussion, and conclusions

On the Minimal Revision Problem of Specification Automata

As robots are being integrated into our daily lives, it becomes necessary to provide guarantees on the safe and provably correct operation. Such guarantees can be provided using automata theoretic task and mission planning where the requirements are expressed as temporal logic specifications. However, in real-life scenarios, it is to be expected that not all user task requirements can be realized by the robot. In such cases, the robot must provide feedback to the user on why it cannot accomplish a given task. Moreover, the robot should indicate what tasks it can accomplish which are as "close" as possible to the initial user intent. This paper establishes that the latter problem, which is referred to as the minimal specification revision problem, is NP complete. A heuristic algorithm is presented that can compute good approximations to the Minimal Revision Problem (MRP) in polynomial time. The experimental study of the algorithm demonstrates that in most problem instances the heuristic algorithm actually returns the optimal solution. Finally, some cases where the algorithm does not return the optimal solution are presented.Comment: 23 pages, 16 figures, 2 tables, International Joural of Robotics Research 2014 Major Revision (submitted

We, Vertiport 6, are temporarily closed: Interactional Ontological Methods for Changing the Destination

This paper presents a continuation of the previous research on the interaction between a human traffic manager and the UATMS. In particular, we focus on the automation of the process of handling a vertiport outage, which was partially covered in the previous work. Once the manager reports that a vertiport is out of service, which means landings for all corresponding agents are prohibited, the air traffic system automates what it has to handle for this event. The entire process is simulated through knowledge representation and reasoning. Moreover, two distinct perspectives are respected for the human supervisor and the management system, and the related ontologies and rules address their interactions. We believe that applying non-monotonic reasoning can verify each step of the process and explain how the system works. After a short introduction with related works, this paper continues with problem formulation, primary solution, discussion, and conclusions.Comment: 8 pages, 1 figure, submitted to IEEERO-MAN (RO-MAN 2023) Workshop on Ontologies for Autonomous Robotics (RobOntics

Ruggedness and Interlaboratory Studies for Asphalt Mixture Performance Tester (AMPT) Cyclic Fatigue Test: Phase \u2161 Report

DTFH6117C00037This report highlights findings from Phase \u2161 of a project designed to develop precision statements for cyclic fatigue testing using an asphalt mixture performance tester (AMPT). These standards include American Association of State Highway and Transportation Officials (AASHTO) TP 107-22 and AASHTO TP 133-21, which apply to 100-mm-diameter and 38-mm-diameter test specimens, respectively.(1,2) Seven laboratories participated in an interlaboratory study of the AMPT cyclic fatigue tests designed according to ASTM E691-20 and ASTM C670-15(4,5) . Researchers in these laboratories conducted dynamic modulus and cyclic fatigue testing of three mixtures for each specimen geometry. The results were used to establish repeatability and reproducibility precision limits for damage characteristic curve and failure criterion results of the AMPT cyclic fatigue tests; these limits reflect the variation in test results that will be exceeded with a probability of 5 percent if the test is executed properly. The researchers introduced a refined functional data metric to capture the variation in damage characteristic curve results. All precision limits were defined as a function of the mixture nominal maximum aggregate size (NMAS) except the failure criterion reproducibility, which did not follow a consistent trend with respect to NMAS. The established repeatability limits quantify the acceptable variation among three test determinations (specimens) obtained within a laboratory on a single material. The reproducibility limits quantify the acceptable variation between average test results of two laboratories conducted on the same material

Relative contributions of the host genome, microbiome, and environment to the metabolic profile

Background Metabolic syndrome is as a well-known risk factor for cardiovascular disease, which is associated with both genetic and environmental factors. Recently, the microbiome composition has been shown to affect the development of metabolic syndrome. Thus, it is expected that the complex interplay among host genetics, the microbiome, and environmental factors could affect metabolic syndrome. Objective To evaluate the relative contributions of genetic, microbiome, and environmental factors to metabolic syndrome using statistical approaches. Methods Data from the prospective Korean Association REsource project cohort (N = 8476) were used in this study, including single-nucleotide polymorphisms, phenotypes and lifestyle factors, and the urine-derived microbial composition. The effect of each data source on metabolic phenotypes was evaluated using a heritability estimation approach and a prediction model separately. We further experimented with various types of metagenomic relationship matrices to estimate the phenotypic variance explained by the microbiome. Results With the heritability estimation, five of the 11 metabolic phenotypes were significantly associated with metagenome-wide similarity. We found significant heritability for fasting glucose (4.8%), high-density lipoprotein cholesterol (4.9%), waist-hip ratio (7.7%), and waist circumference (5.6%). Microbiome compositions provided more accurate estimations than genetic factors for the same sample size. In the prediction model, the contribution of each source to the prediction accuracy varied for each phenotype. Conclusion The effects of host genetics, the metagenome, and environmental factors on metabolic syndrome were minimal. Our statistical analysis suffers from a small sample size, and the measurement error is expected to be substantial. Further analysis is necessary to quantify the effects with better accuracy.N

A Controllable Agent by Subgoals in Path Planning Using Goal-Conditioned Reinforcement Learning

The aim of path planning is to search for a path from the starting point to the goal. Numerous studies, however, have dealt with a single predefined goal. That is, an agent who has completed learning cannot reach other goals that have not been visited in the training. In the present study, we propose a novel reinforcement learning (RL) framework for an agent reachable to any subgoal as well as the final goal in path planning. To do this, we utilize goal-conditioned RL and propose bidirectional memory editing to obtain various bidirectional trajectories of the agent. Bidirectional memory editing can generate various behavior and subgoals of the agent from the limited trajectory. Then, the generated subgoals and behaviors of the agent are trained on the policy network so that the agent can reach any subgoals from any starting point. In addition, we present reward shaping for the short path of the agent to reach the goal. In the experimental result, the agent was able to reach the various goals that had never been visited by the agent during the training. We confirmed that the agent could perform difficult missions, such as a round trip, and the agent used the shorter route with reward shaping