In August 2014, I wrote a column about artificial evolution. The focus was on the creation of new forms of life, biological or digital, which would then evolve at incredible speeds. The emphasis then was on the software part of life: the algorithms that gave the appearance of intelligence. Recent reports from MIT's CSAIL (computer science and artificial intelligence lab) indicate that researchers were able to create a working robot using a 3D printer. Creating inexpensive robots in itself is not terribly novel.
Grau, Bernardo Cuenca (University of Oxford) | Jimenez-Ruiz, Ernesto (University of Oxford) | Kharlamov, Evgeny (Free University of Bozen-Bolzano) | Zheleznyakov, Dmitriy (Free University of Bozen-Bolzano)
The dynamic nature of ontology development has motivated the formal study of ontology evolution problems. This paper presents a logical framework that enables fine-grained investigation of evolution problems at a deductive level. In our framework, the optimal evolutions of an ontology O are those ontologies O′ that maximally preserve both the structure of O and its entailments in a given preservation language. We show that our framework is compatible with the postulates of Belief Revision, and we investigate the existence of optimal evolutions in various settings.
Portfolio Online Evolution is a novel method for playing real-time strategy games through evolutionary search in the space of assignments of scripts to individual game units. This method builds on and recombines two recently devised methods for playing multi-action games: (1) Portfolio Greedy Search, which searches in the space of heuristics assigned to units rather than in the space of actions, and (2) Online Evolution, which uses evolution rather than tree search to effectively play games where multiple actions per turn lead to enormous branching factors. The combination of both ideas lead to the use of evolution to search the space of which script/heuristic is assigned to which unit. In this paper, we introduce the ideas of Portfolio Online Evolution and apply it to StarCraft micro, or individual battles. It is shown to outperform all other tested methods in battles of moderate to large size.