The idea behind interactive evolutionary algorithms is due to Richard Dawkins. Humans are not limited to the service computation of evaluator or some other predefined role, but can choose to perform a more diverse set of tasks. Human-based computation methods combine computers and humans in different roles. The following table uses the evolutionary computation model to describe four classes of computation, three of which rely on humans in some role.
For each class, a representative example is shown. Classes of human-based computation from this table can be referred by two-letter abbreviations: HC, CH, HH. Here the first letter identifies the type of agents performing innovation, the second letter specifies the type of selection agents. Authors of the programs copy, modify, and recombine successful strategies to improve their chances of winning. Simulated breeding style introduces no explicit fitness, just selection, which is easier for humans.
However, the selection mechanism was absent until 2002, when wiki has been augmented with a revision history allowing for reversing of unhelpful changes. Social search applications accept contributions from users and attempt to use human evaluation to select the fittest contributions that get to the top of the list. These use one type of human-based innovation. Early work was done in the context of HBGA. Digg and Reddit are recently popular examples. A computer generates a problem and presents it to evaluate a user.
For example, CAPTCHA tells human users from computer programs by presenting a problem that is supposedly easy for a human and difficult for a computer. While CAPTCHAs are effective security measures for preventing automated abuse of online services, the human effort spent solving them is otherwise wasted. Natural Human Computation involves leveraging existing human behavior to extract computationally significant work without disturbing that behavior. The UNU platform for human swarming establishes real-time closed-loop systems around groups of networked users molded after biological swarms, enabling human participants to behave as a unified collective intelligence.
In different human-based computation projects people are motivated by one or more of the following. Many projects had explored various combinations of these incentives. The latter depend on obligations to maintain their more or less fixed structure, be functional and stable. Each of them is similar to a carefully designed mechanism with humans as its parts.
However, this limits the freedom of their human employees and subjects them to various kinds of stresses. The algorithmic outsourcing techniques used in human-based computation are much more scalable than the manual or automated techniques used to manage outsourcing traditionally. It is this scalability that allows to easily distribute the effort among thousands of participants. As a result, many employers attempt to manage worker automatically through algorithms rather than responding to workers on a case-by-case basis or addressing their concerns.
Human assistance can be helpful in solving any AI-complete problem, which by definition is a task which is infeasible for computers to do but feasible for humans. Internet search, improving ranking of results by combining automated ranking with human editorial input. Tracking a Criminal Suspect through “Face-Space” with a Genetic Algorithm, in Proceedings of the Fourth International Conference on Genetic Algorithm, Morgan Kaufmann Publisher, pp. A human-centered semantic service platform for the digital ecosystems environment. UCOSAIS: A framework for user-centered online service advertising information search.