Just as people from different countries speak different languages, AI models also create various internal "languages"—a unique set of tokens understood only by each model. Until recently, there was no ...

Creating the rational-programmer algorithms as well as the representative scenario corpus requires problem-specific research; the experimental setup, though, remains the same. See Figure 1 for an ...

Approximate dynamic programming (ADP), as a real-time optimization algorithm, has been applied in areas such as rail transit scheduling (Nguyen and Chow, 2023) and electric vehicle frequency ...

The simplicity, precision, stability, and versatility of such dynamic temporal programming circuits greatly expand the potential of artificial molecular reaction networks for more complex practical ...

This study examines the use of greedy algorithms, dynamic programming algorithms, and lattice discretization algorithms for solving optimal solutions in practical scientific and engineering ...

Dynamic Programming is a paradigm of algorithm design in which an optimization problem is solved by a combination of achieving sub-problem solutions and appearing to the "principle of optimality".

This article develops a novel online adaptive dynamic programming algorithm with adjustable convergence rate to address the optimal control problem of nonlinear systems. Relaxation factors are ...

Dynamic Programming is a paradigm of algorithm design in which an optimization problem is solved by a combination of achieving sub-problem solutions and appearing to the "principle of optimality".