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This chapter is about domain-independent classical-planning algorithms, which until recently were the most widely studied class of AI planning algorithms. The chapter classifies and describes a variety of forward search, backward search, and plan-space planning algorithms, as well as heuristics for guiding the algorithms.
This chapter sets the foundation for the next two chapters. It introduces the reader to robotics platforms for the development of acting, planning, and learning functions. The study of motion is based on classical mechanics for the modeling of forces and their effects on mouvements. Robotics builds on this knowledge to master computational motion, navigation, and manipulation over different types of devices and environments. Robotic devices are informally introduced in the following section. Motion problems and the metric representations with continuous state variables needed for geometric, kinematic, and dynamic operational models are then presented. Section 20.3 introduces localization and navigation problems, followed by a section on manipulation problems and their representations.
This chapter is about representing HTN planning domains and solving HTN planning problems. Several of the formal definitions require the same "classical planning" restrictions as in Part I, but most practical HTN implementations loosen or drop several of these restrictions. We first discuss ways to represent and solve planning problems in which there is a totally ordered sequence of tasks to accomplish. We then generalize to allow partially ordered tasks and describe ways to combine classical planning and HTN planning. Finally, we briefly discuss heuristic functions, expressivity, and computational complexity.
We have established a novel molecular kinetic model that addresses fundamental challenges in the non-equilibrium transport of nanoscale confined fluids, such as rarefaction and fluid inhomogeneities, which are crucial to a range of scientific and engineering fields. The proposed model explicitly considers fluid–solid molecular interactions in the transport equations, eliminating the reliance on predefined boundary conditions. By consistently accounting for molecular interactions between fluids and solids, the unified model captures both intrinsic and apparent non-hydrodynamic effects, as well as real fluid behaviours. Rigorous comparisons with molecular dynamics simulations demonstrate that the present model accurately predicts unique features of strongly inhomogeneous fluid flows, including fluid adsorption, solvation force, velocity slip and temperature jump. Therefore, this mesoscopic model bridges the gap between molecular-scale dynamics and macroscopic hydrodynamics, enabling a practical simulation tool for nanoscale surface-confined flows. Moreover, it offers valuable insights into the molecular mechanisms underlying anomalous transport phenomena observed in confined flows, such as the disappearance and re-emergence of the Knudsen minimum.
Large-eddy simulations have been conducted to investigate the decay law of homogeneous turbulence influenced by a magnetic field within a cubic domain, employing periodic boundary conditions. The initial integral Reynolds number is approximately 1000, while the initial interaction number $N$ ranges from 0.1–100. The results reveal that the Joule cone angle $\theta$, half of the Joule cone, decays as $\cos \theta \sim t^{-1/2}$ when $N \gg 1$. In the nonlinear stage, small-scale vortices gradually recover and restore three-dimensionality. Moreover, the corresponding critical state at small scales, marking the transition from quasi-two-dimensional structure to the onset of three-dimensionality, has been quantitatively defined. During the linear stage, based on the true magnetic damping number ($\tau _t = \rho / (\sigma {\boldsymbol{B}}^2 \cos ^2 \psi )$, where $\sigma$, $\boldsymbol{B}$ and $\psi$ denote the electrical conductivity, magnetic field and the angle between the wavevector and $\boldsymbol{B}$ in Fourier space, respectively), Moffatt’s decay law, $K \sim t^{-1/2}$, manifests at distinct times and zones in the Fourier space, with $K$ signifying turbulent kinetic energy. In the nonlinear stage, for $N \gg 1$, a $-3$ slope in the energy power spectrum is prominently observed over an extended period. The near-equivalence of the characteristic time scales of inertial and Lorentz forces in the inertial subrange suggests a quasiequilibrium state between energy transfer and Joule dissipation in Fourier space, thereby corroborating the hypothesis proposed by Alemany et al. 1979 Journal de Mecanique18(2): 277–313. Additionally, it is observed that pressure mediates energy transfer from horizontal kinetic energy ($K_{\parallel }$) to vertical kinetic energy ($K_{\bot }$), accelerating the decay of $K_{\parallel }$. Notably, concurrent inverse and direct energy transfers emerge during the decay process. Our analysis reveals that the ratio $R$ of the maximum inverse to maximum direct energy flux correlates with the dimensionality of the turbulence, following the scaling law $R\sim (\cos \theta )^{-2.2}$.
The chapters in Part II are about algorithms for planning, acting, and learning using hierarchical task networks (HTNs). HTNs can describe ways to perform complex tasks without the overhead of searching through a large state space, how to avoid situations where unanticipated events are likely to cause bad outcomes, and how to recover when unanticipated events occur.
Flow over bluff bodies encounters instability at supercritical Reynolds numbers, exhibiting the periodic vortex shedding that leads to structural vibrations and acoustic noise. In this paper, a new aerodynamic shape optimisation strategy based on resolvent analysis is proposed to passively control the vortex shedding over two-dimensional cylinders. Firstly, we show that when the flow satisfies the rank-1 approximation, minimizing the maximal resolvent gain enhances flow stability. Secondly, we formulate the geometry-constrained resolvent-based optimisation problem that can be solved by the nonlinear conjugate gradient algorithm. Compared with conventional stability-based optimisation, the proposed approach is more effective as it avoids the cumbersome eigendecomposition of the high-dimensional Jacobian matrix. The efficacy of the proposed resolvent-based optimisation is validated through improving the stability of the one-dimensional Ginzburg–Landau equation. Thirdly, this approach is applied to suppress the vortex shedding of bluff bodies, initialised by a circular cylinder. Although the optimisation is performed at a subcritical state $Re = 40$, reduced vortex shedding and drag forces can be achieved at supercritical Reynolds numbers, while the critical Reynolds number is extended from $47$ to $60$. Dynamic mode decomposition is then performed to reveal that the optimised system becomes more stable and satisfies the rank-1 approximation. Finally, we demonstrate that the combined effects of the flattened surface and the Coanda effect delay flow separation, keeping the separation point nearly unchanged at supercritical Reynolds numbers (e.g. between 80 and 140) for the optimised geometry. This results in a substantial reduction in the strength of vortex shedding, which in turn leads to decreased drag forces. The optimised shape still achieves drag reduction in turbulent flows at a relatively high Reynolds number.
This chapter discusses several ways for actors to use HTN domain models. These include a way to use HTN methods for purely reactive acting, some simple ways for an actor to make use of an HTN planner, and some ways to repair HTN plans when unexpected events occur during acting.
The hierarchical refinement approach in the previous two chapters requires a priori domain knowledge of the methods, action models, and heuristics used by RAE and UPOM. The topic of this chapter is to use machine learning techniques to synthesize planning heuristics and domain knowledge. It illustrates the "planning to learn" paradigm for learning domain-dependent heuristics to guide RAE and UPOM. Given methods and a sample function, UPOM generates near-optimal choices that are taken as targets by a deep Q-learning procedure. The chapter shows how to synthesize methods for tasks using hierarchical reinforcement techniques.
We derive boundary conditions for two-dimensional parallel and non-parallel flows at the interface of a homogeneous and isotropic porous medium and an overlying fluid layer by solving a macroscopic closure problem based on the asymptotic solution to the generalised transport equations (GTE) in the interfacial region. We obtained jump boundary conditions at the effective sharp surface dividing the homogeneous fluid and porous layers for either the Darcy or the Darcy–Brinkman equations. We discuss the choice of the location of the dividing surface and propose choices which reduce the distance with the GTE solutions. We propose an ad hoc expression of the permeability distribution within the interfacial region which enables us to preserve the invariance of the fluid-side-averaged velocity profile with respect to the radius $r_0$ of the averaging volume. Solutions to the GTE, equipped with the proposed permeability distribution, compare favourably with the averaged solutions to the pore-scale simulations when the interfacial thickness $\delta$ is adjusted to $r_0$. Numerical tests for parallel and non-parallel flows using the obtained jump boundary conditions or the generalised transport equations show quantitative agreement with the GTE solutions, with experiments and pore-scale simulations. The proposed model of mass and momentum transport is predictive, requiring solely information on the bulk porosity and permeability and the location of the solid matrix of the porous medium. Our results suggest that the Brinkman corrections may be avoided if the ratio $a=\delta /\delta _B$ of the thickness $\delta$ of the interfacial region to the Brinkman penetration depth $\delta _B$ is large enough, as the Brinkman sub-layer is entirely contained within the interfacial region in that case. Our formulation has been extended to anisotropic porous media and can be easily dealt with for three-dimensional configurations.