Optimization in Knowledge-Intensive Crowdsourcing

We present SmartCrowd, a framework for optimizing collaborative knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by accounting for human factors in the process of assigning tasks to workers. Human factors designate workers' expertise in different skills, their expected minimum wage, and their availability. In SmartCrowd, we formulate task assignment as an optimization problem, and rely on pre-indexing workers and maintaining the indexes adaptively, in such a way that the task assignment process gets optimized both qualitatively, and computation time-wise. We present rigorous theoretical analyses of the optimization problem and propose optimal and approximation algorithms. We finally perform extensive performance and quality experiments using real and synthetic data to demonstrate that adaptive indexing in SmartCrowd is necessary to achieve efficient high quality task assignment.Comment: 12 page

Cinnamons: A Computation Model Underlying Control Network Programming

We give the easily recognizable name "cinnamon" and "cinnamon programming" to a new computation model intended to form a theoretical foundation for Control Network Programming (CNP). CNP has established itself as a programming paradigm combining declarative and imperative features, built-in search engine, powerful tools for search control that allow easy, intuitive, visual development of heuristic, nondeterministic, and randomized solutions. We define rigorously the syntax and semantics of the new model of computation, at the same time trying to keep clear the intuition behind and to include enough examples. The purposely simplified theoretical model is then compared to both WHILE-programs (thus demonstrating its Turing-completeness), and the "real" CNP. Finally, future research possibilities are mentioned that would eventually extend the cinnamon programming into the directions of nondeterminism, randomness, and fuzziness.Comment: 7th Intl Conf. on Computer Science, Engineering & Applications (ICCSEA 2017) September 23~24, 2017, Copenhagen, Denmar

Interactive Small-Step Algorithms I: Axiomatization

In earlier work, the Abstract State Machine Thesis -- that arbitrary algorithms are behaviorally equivalent to abstract state machines -- was established for several classes of algorithms, including ordinary, interactive, small-step algorithms. This was accomplished on the basis of axiomatizations of these classes of algorithms. Here we extend the axiomatization and, in a companion paper, the proof, to cover interactive small-step algorithms that are not necessarily ordinary. This means that the algorithms (1) can complete a step without necessarily waiting for replies to all queries from that step and (2) can use not only the environment's replies but also the order in which the replies were received