Textbooks on algorithms for optimization, decision making, and validation (algorithmsbook.com)

MIT Press has published a linked set of textbooks by Mykel J. Kochenderfer and colleagues (2019, 2022, and a more recent preview edition) that collectively cover algorithms for optimization, decision making under uncertainty, and validation of autonomous and learning systems. These volumes synthesize theoretical foundations with practical algorithmic recipes, making them a go-to resource for graduate students, researchers, and practitioners building decision-making systems—from robotics and autonomous vehicles to AI-driven control and planning. The appearance of multiple editions and collaborators underscores the maturation of this material into a coherent curriculum for safe, reliable AI systems. Technically, the books span core tools used across AI/ML: optimization algorithms (both continuous and combinatorial), stochastic decision frameworks (MDPs, POMDPs, planning, and reinforcement learning), and methods for validation and verification (simulation-based testing, statistical evaluation, and safety assurance). Emphasis is on algorithmic structure, computational trade-offs, and how to apply approximate and exact methods in practice, helping readers bridge theory to implementation. For the community, these texts consolidate best practices for designing, tuning, and validating decision-making pipelines—critical as deployed AI systems demand provable performance and robust evaluation under uncertainty.