Multi-agent Simulation for AI Behaviour Discovery in Operations Research

Papasimeon, Michael; Benke, Lyndon

doi:10.1007/978-3-030-94548-0_6

Cited by 2 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quality of these new plan stems is evaluated, and any that meet the required minimum quality q are added to the open list (lines 11-15). If π was not expanded, then the plan has reached a leaf node, and if it meets the specified diversity requirement d, the complete plan is added to the final set of top plans P (lines [17][18][19]. Ties in plan quality are broken using diversity by continuing to evaluate plans until a lower-quality plan is found (lines 20-28).…”

Section: Plan Extraction Algorithmmentioning

confidence: 99%

“…Planners that produce plan sets have applications in a wide range of fields, including planning for problems with incomplete or unknown user preferences [18], scenario prediction for risk management [27], plan recognition [28], plan repair [10], and explanation generation [5]. Our research is motivated by the discovery of novel agent behaviours in the context of operations research [16,19,23], recently proposed as an input to deceptive mission planning [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diverse, Top-k, and Top-Quality Planning Over Simulators

Benke,

Miller,

Papasimeon

et al. 2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classical planners that require a symbolic model of the problem instance. This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS), enabling application to problems for which only a black-box simulation model is available. We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. We demonstrate this approach on a path-planning problem with hidden information, and suggest adaptations to the MCTS algorithm to increase the diversity of generated plans. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.

show abstract

Section: Plan Extraction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diverse, Top-k, and Top-Quality Planning Over Simulators

Benke,

Miller,

Papasimeon

et al. 2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…In contrast to their work, we focus primarily on combat within visual range using a range of planner-based behaviour recognition. We apply some of the techniques explored by Vered and Kaminka (2017) to a continuous flight behaviour recognition problem, successfully recognizing a range of behaviours simulated through the ACE0 multi-agent-based-simulation environment (Papasimeon and Benke, 2021) (described in further detail in Section 4).…”

Section: Related Workmentioning

confidence: 99%

“…For the purposes of this work, a light-weight version of ACE, known as ACE0 was employed. Further details on the architecture of ACE0 can be found in the paper by (Papasimeon and Benke, 2021). ACE0 is a minimal subset of ACE representing only two aircraft (or UAV) in 1v1 adversarial scenarios.…”

Section: Simulation Environmentmentioning

confidence: 99%

X-ray diffraction of photovoltaic perovskites: Principles and applications

Tan

McNeill

2022

Applied Physics Reviews

View full text Add to dashboard Cite

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.

show abstract

Multi-agent Simulation for AI Behaviour Discovery in Operations Research

Cited by 2 publications

References 20 publications

Diverse, Top-k, and Top-Quality Planning Over Simulators

Diverse, Top-k, and Top-Quality Planning Over Simulators

X-ray diffraction of photovoltaic perovskites: Principles and applications

Contact Info

Product

Resources

About