TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Practically all programming languages allow the programmer to split a program into several modules which brings along several advantages in software development. In this paper, we are interested in the area of answer-set programming where fully declarative and nonmonotonic languages are applied. In this context, obtaining a modular structure for programs is by no means straightforward since the output of an entire program cannot in general be composed from the output of its components. To better understand the effects of disjunctive information on modularity we restrict the scope of analysis to the case of disjunctive logic programs (DLPs) subject to stable-model semantics. We define the notion of a DLP-function, where a well-defined input/output interface is provided, and establish a novel module theorem which indicates the compositionality of stable-model semantics for DLP-functions. The module theorem extends the well-known splitting-set theorem and enables the decomposition of DLP-functions given their strongly connected components based on positive dependencies induced by rules. In this setting, it is also possible to split shared disjunctive rules among components using a generalized shifting technique. The concept of modular equivalence is introduced for the mutual comparison of DLP-functions using a generalization of a translation-based verification method.

SOLUTION: Modularity Aspects of Disjunctive Stable Models

PROBLEM: There is overwhelming evidence that human intelligence is a product of Darwinian evolution. Investigating the consequences of self-modification, and more precisely, the consequences of utility function self-modification, leads to the stronger claim that not only human, but any form of intelligence is ultimately only possible within evolutionary processes. Humandesigned artificial intelligences can only remain stable until they discover how to manipulate their own utility function. By definition, a human designer cannot prevent a superhuman intelligence from modifying itself, even if protection mechanisms against this action are put in place. Without evolutionary pressure, sufficiently advanced artificial intelligences become inert by simplifying their own utility function. Within evolutionary processes, the implicit utility function is always reducible to persistence, and the control of superhuman intelligences embedded in evolutionary processes is not possible. Mechanisms against utility function self-modification are ultimately futile. Instead, scientific effort toward the mitigation of existential risks from the development of superintelligences should be in two directions: understanding consciousness, and the complex dynamics of evolutionary systems.

SOLUTION: Non-Evolutionary Superintelligences Do Nothing, Eventually

PROBLEM: Case-based reasoning (CBR) based on description logics (DLs) has gained a lot of attention lately. Adaptation is a basic task in CBR that can be modeled as a knowledge base revision problem which has been solved in propositional logic. However, in DLs, adaptation is still a challenge problem since existing revision operators only work well for DLs of the DL-Lite family. It is difficult to design revision algorithms that are syntax-independent and fine-grained. In this paper, we present a new method for adaptation based on the tractable DL EL⊥. Following the idea of adaptation as revision, we firstly extend the logical basis for describing cases from propositional logic to the DL EL⊥, and then present a formalism for adaptation based on EL⊥. With this formalism, we show that existing revision operators and algorithms in DLs do not work for it, and then present our adaptation algorithm. Our algorithm is syntax-independent and fine-grained, and satisfies the requirements on revision operators.

SOLUTION:
Algorithm for Adapting Cases Represented in a Tractable Description Logic