Invariants of solvable Lie algebras with triangular nilradicals and diagonal nilindependent elements

The invariants of solvable Lie algebras with nilradicals isomorphic to the algebra of strongly upper triangular matrices and diagonal nilindependent elements are studied exhaustively. Bases of the invariant sets of all such algebras are constructed by an original purely algebraic algorithm based on Cartan's method of moving frames.Comment: 21 pages, enhanced and extended version. Section 2 reviews the method of finding invariants of Lie algebras that was proposed in arXiv:math-ph/0602046 and arXiv:math-ph/0606045. The computation is based on developing a specific technique given in arXiv:0704.0937. Results generalize ones of arXiv:0705.2394 to the case of arbitrary relevant number of nilindependent element

Invariants of Lie Algebras with Fixed Structure of Nilradicals

An algebraic algorithm is developed for computation of invariants ('generalized Casimir operators') of general Lie algebras over the real or complex number field. Its main tools are the Cartan's method of moving frames and the knowledge of the group of inner automorphisms of each Lie algebra. Unlike the first application of the algorithm in [J. Phys. A: Math. Gen., 2006, V.39, 5749; math-ph/0602046], which deals with low-dimensional Lie algebras, here the effectiveness of the algorithm is demonstrated by its application to computation of invariants of solvable Lie algebras of general dimension $n<\infty$ restricted only by a required structure of the nilradical. Specifically, invariants are calculated here for families of real/complex solvable Lie algebras. These families contain, with only a few exceptions, all the solvable Lie algebras of specific dimensions, for whom the invariants are found in the literature.Comment: LaTeX2e, 19 page

FoCaLiZe: Inside an F-IDE

For years, Integrated Development Environments have demonstrated their usefulness in order to ease the development of software. High-level security or safety systems require proofs of compliance to standards, based on analyses such as code review and, increasingly nowadays, formal proofs of conformance to specifications. This implies mixing computational and logical aspects all along the development, which naturally raises the need for a notion of Formal IDE. This paper examines the FoCaLiZe environment and explores the implementation issues raised by the decision to provide a single language to express specification properties, source code and machine-checked proofs while allowing incremental development and code reusability. Such features create strong dependencies between functions, properties and proofs, and impose an particular compilation scheme, which is described here. The compilation results are runnable OCaml code and a checkable Coq term. All these points are illustrated through a running example.Comment: In Proceedings F-IDE 2014, arXiv:1404.578

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat