Artificial Intelligence is considered one of the most revolutionary developments in the history of technology. Within a few years, the world has already witnessed the transformative capabilities of this tech. Not to our surprise, AI is already driving several innovations and powering some of the most cutting-edge everyday solutions. Already, a captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are stirring controversies where the world's leading experts disagree such as AI's future impact on the job market; what will happen if human-level AI will be developed, will it lead to an intelligence explosion, and whether we should welcome or fear this advancement.

A fundamental question in AI is what mechanisms suffice for computer programs to make scientific discoveries. My Ph.D. thesis (Valdés-Pérez 1990e) addresses this question by automating the following scientific task to a significant extent: Given observed data about a particular chemical reaction, discover the underlying set of reaction steps from starting materials to products, that is, elucidate the reaction pathway. My scientific contribution is to describe and interpret the design of a system that forms plausible explanatory hypotheses about dynamic processes in science and that proposes unseen entities in a manner justified by simplicity. Some byproducts of the thesis are several novel contributions to chemistry knowledge in addition to scientific tools of immediate use. Chapter 1 surveys previous work in machine discovery, focusing on work that involved assembling an extensive amount of knowledge particular to a domain.

Admissible heuristics are an important class of heuristics worth discovering: they guarantee shortest path solutions in search algorithms such asA* and they guarantee less expensively produced, but boundedly longer solutions in search algorithms such as dynamic weighting. Unfortunately, effective (accurate and cheap to compute) admissible heuristics can take years for people to discover. Several researchers have suggested that certain transformations of a problem can be used to generate admissible heuristics. This article defines a more general class of transformations, calledabstractions, that are guaranteed to generate only admissible heuristics. It also describes and evaluates an implemented program (Absolver II) that uses a means-ends analysis search control strategy to discover abstracted problems that result in effective admissible heuristics.

A fundamental question in AI is what mechanisms suffice for computer programs to make scientific discoveries. My Ph.D. thesis addresses this question by automating the following scientific task to a significant extent: Given observed data about a particular chemical reaction, discover the underlying set of reaction steps from starting materials to products, that is, elucidate the reaction pathway.

A fundamental question in AI is what mechanisms suffice for computer programs to make scientific discoveries. My Ph.D. thesis addresses this question by automating the following scientific task to a significant extent: Given observed data about a particular chemical reaction, discover the underlying set of reaction steps from starting materials to products, that is, elucidate the reaction pathway.