Case-Based Policy and Goal Recognition

Borck, Hayley; Karneeb, Justin; Floyd, Michael W.; Alford, Ron; Aha, David W.

doi:10.1007/978-3-319-24586-7_3

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The TBM's agent interpreter recognizes the behaviors of hostile aircraft using its models of them (M A ). We showed that a case-based reasoning technique (Borck et al 2015), which uses features that model an agent's prior behaviors, performs well on this task. We later extended it with an active planner that "teases out" a hostile's behavior (to disambiguate it from others) and showed that this active planner further improves performance (Alford et al 2015).…”

Section: Oceanserveriver2mentioning

confidence: 98%

Goal Reasoning: Foundations, Emerging Applications, and Prospects

Aha¹

2018

AI Magazine

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Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.

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Section: Oceanserveriver2mentioning

confidence: 98%

Goal Reasoning: Foundations, Emerging Applications, and Prospects

Aha¹

2018

AI Magazine

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“…Behaviour recognition in beyond visual range (BVR) air combat has also been much researched. Borck et al (2015b) used case based behaviour recognition. The set of actions the agent attempts to recognize are: pursuit, whereby an agent flies directly at another agent, drag, whereby an agent tries to kinematically avoid a missile by flying away from it, and crank, whereby an agent flies at the maximum offset but tries to keep its target in radar.…”

Section: Related Workmentioning

confidence: 99%

X-ray diffraction of photovoltaic perovskites: Principles and applications

Tan

McNeill

2022

Applied Physics Reviews

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Solar cells based on organic–inorganic hybrid perovskite materials have emerged as the most efficient next-generation thin-film solar cells within just a decade of research and show great promise for commercialization. As control of the thin-film microstructure of the perovskite layer is a key factor enabling high photovoltaic efficiency, good stability, and successful up-scaling of high-quality perovskite thin films for commercialization, a reliable and accurate characterization of the thin-film microstructure is paramount. X-ray diffraction (XRD)-based techniques, including conventional laboratory-based XRD and synchrotron-based grazing-incidence wide-angle x-ray scattering, are widely used to probe the microstructure of photovoltaic perovskite thin films. Nevertheless, it is common for these XRD experiments to be poorly executed and diffraction data to be improperly interpreted. This review focuses on principles of XRD techniques and their application for the characterization of the perovskite thin-film microstructure. Fundamentals of XRD techniques are presented with a strong emphasis on best practices in data collection and analysis. Approaches for the reliable and accurate extraction of microstructural information from diffraction data are discussed, including the need for simulating diffraction patterns. Applications of XRD techniques in characterizing perovskite thin films are demonstrated for both three-dimensional and layered hybrid perovskites, covering various microstructural aspects including phase identification and quantification, texture analysis, microstrain, and macrostrain as well as in situ and operando characterization. The additional subtleties and complexities associated with the XRD characterization of layered hybrid perovskites due to a more complex thin-film microstructure are discussed. Common mistakes and pitfalls that lead to misinterpretation of diffraction data are also highlighted.

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“…In the past, researchers have mostly focused on the problem of target activity identification in air situation understanding [19]- [22]. Undoubtedly, activity recognition is of great help in enriching intention recognition knowledge.…”

Section: Introductionmentioning

confidence: 99%

Robust Air Target Intention Recognition Based on Weight Self-Learning Parallel Time-Channel Transformer Encoder

Song,

Zhou,

Cheng

et al. 2023

IEEE Access

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Most existing air target intention recognition methods use only single-moment information, risking failure when acquiring data containing noise and many outliers. The robustness of methods that utilize continuous moment information has yet to be explored. This paper designs a robust recognition method for air target intention to address the above problems. The method takes data with noise and outliers as the object, based on a parallel time-channel Transformer Encoder and a weight self-learning unit. First, a detailed introduction to air target intention recognition and robust recognition is given, and the intention space and feature space are defined. Subsequently, the data samples are reconstructed using a fixed-step sliding window to increase the information utilized with multi-moment information as input. Finally, step-wise and channelwise correlations are extracted using a time-axis Transformer Encoder and a channel-axis Transformer Encoder, respectively, and the weights of the two branches are automatically learnt using a weight selflearning unit. This enhanced self-attention network allocates attention weights between elements in the time and channel domain sequences to capture their long-range and short-range relationships and extract recognizable representations, making it robust to outliers and noise. The experimental results show that the model's recognition accuracy and composite F1 score reach 96.9% and 0.9676, and its performance remains well when the noise level and outliers proportion increase. The ablation and comparison experiments show its advantage in accuracy over other models.

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Case-Based Policy and Goal Recognition

Cited by 6 publications

References 14 publications

Goal Reasoning: Foundations, Emerging Applications, and Prospects

Goal Reasoning: Foundations, Emerging Applications, and Prospects

X-ray diffraction of photovoltaic perovskites: Principles and applications

Robust Air Target Intention Recognition Based on Weight Self-Learning Parallel Time-Channel Transformer Encoder

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