Abstract Machine-learned systems are in widespread use for making decisions about humans, and it is important that they fair , i.e., not biased against individuals based on sensitive attributes. We present runtime verification of algorithmic fairness whose models unknown, but assumed to have a Markov chain structure. introduce specification language can model many common properties, such as dem...

The Program Development Environment PDE is a tool for massively parallel programming of distributed-memory architectures. Adopting a knowledge-based approach, the PDE eliminates the complexity introduced by parallel hardware with distributed memory and offers complete transparency in respect of parallelism exploitation. The knowledge-based part of the PDE is realized in CLIPS. Its principal tas...

The way super- and sub-additive transformations of aggregation functions are introduced involve suprema and infima taken over simplexes whose dimensions may grow arbitrarily. Exact values of such transformations may thus be hard to determine in general. In this note we discuss methods of algorithmic approximation of such transformations.

Expert systems use explicitly coded knowledge (often in the form of IF·THEN rules) to solve problems for which a (numerical) algorithmic solution is not appropriate. There are many specialized expert system development environments available. T AXSY is an expert system shell written completely in SAS. It consists of a set of SAS programs which, with the addition of datasets with rules and code,...

Designing and implementing AI in games is an interesting, yet complex task. This paper briefly presents some applications that make use of Answer Set Programming for such a task, and show some advantages of declarative programming frameworks against imperative (algorithmic) approaches while dealing with knowledge representation and reasoning: solid theoretical bases, no need for algorithm desig...

Machine learning is widely regarded as a tool for overcoming the bottleneck in knowledge acquisition. Especially in knowledge-intensive domains there is the hope for using machine learning techniques successfully. This paper prove the general inability of simple learning programs to learn complex concepts from few input data. This holds independently of the epistemological problems of inductive...

Protein structure prediction is one of the major challenges in bioinformatics today. Throughout the past five decades, many different algorithmic approaches have been attempted, and although progress has been made the problem remains unsolvable even for many small proteins. While the general objective is to predict the three-dimensional structure from primary sequence, our current knowledge and...