Human-based computation

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In computer science, human-based computation is a technique when a computational process performs its function via outsourcing certain steps to humans. This approach explores differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human-computer interaction.

In traditional computation, a human employs a computer to solve a problem: a human provides a formalized problem description to a computer, and receives a solution to interpret. In human-based computation, the roles are often reversed: the computer asks a person or a large number of people to solve a problem, then collects, interprets, and integrates their solutions.

Contents 1 Early work 2 Classes of human-based computation 3 Methods of human-based computation 4 Incentives to participation 5 Human-based computation as a form of social organization 6 See also 7 References 8 External links

[edit] Early work

Early work implementing this concept was made by Victor Johnston and Karl Sims in 1991 in the context of evolutionary computation (EC). They pioneered the use of human evaluation in an evolutionary computation, by asking humans to compute fitness function using their visual perception and esthetic ability. As a result, their programs could evolve beautiful faces and pieces of art. These programs effectively reversed the common role assignment between computer and human. Computer is no longer an agent of its user, but instead, a coordinator and an aggregator of efforts of many human evaluators. The approach was extended in 1998 in Human-based genetic algorithm (HBGA). In HBGA, humans are not limited to the role of evaluator, but perform other functions. They can contribute their innovative solutions into the evolutionary process, make incremental changes to existing solutions, and perform intelligent recombination. In short, an HBGA outsources to humans all operations of a typical genetic algorithm and can be viewed as a form of social organization and a way to organize collaboration (Kosorukoff and Goldberg, 2002). As a result, HBGA could identify problems and evolve solutions expressed in natural language, obviating the need for some fixed representation that was a limiting factor in many potential social applications of EC. In addition, HBGA was proposed as a general model unifying different techniques of human-based computation.

[edit] Classes of human-based computation

Human-based computation methods combine computers and humans in different roles. Kosorukoff (2000) proposed a way to describe division of labor in computation, that groups human-based methods into three classes. 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. The classification is in terms of the roles (innovation or selection) performed in each case by humans and computational processes. This table is a slice of three-dimensional table. The third dimension defines if the organizational function is performed by humans or a computer. Here it is assumed to be performed by a computer.

Division of labor in computation Selection agent Innovation agent Computer Human Computer Genetic Algorithm Interactive genetic algorithm Human Computerized Tests Human-based genetic algorithm

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. In some implementations (wiki is the most common example), human-bassed selection functionality might be limited, it can be shown with small h.

[edit] Methods of human-based computation

• (HC) Darwin (Vyssotsky, Morris, McIlroy, 1961) and Core War (Jones, Dewdney 1984) These are games where several programs written by people compete in a tournament (computational simulation) in which fittest programs will survive. Authors of the programs copy, modify, and recombine successful strategies to improve their chances of winning.

• (CH) Interactive EC (Caldwell and Johnston, 1991; Sims, 1991) IEC enables the user to create an abstract drawing only by selecting his/her favorite images, so human only performs fitness computation and software performs innovative role. [Unemi 1998] Simulated breeding style introduces no explicit fitness, just selection, which is easier for humans.

• (HH₂) Wiki (Cunningham, 1995) enabled editing the web content by multiple users, i.e. supported two types of human-based innovation (contributing new page and its incremental edits). However, the selection mechanism was absent until 2002, when wiki has been augmented with a revision history allowing for reversing of unhelpful changes. This provided means for selection among several versions of the same page and turned wiki into a tool supporting collaborative content evolution (would be classified as human-based evolution strategy in EC terms).

• (HH₃) Human-based genetic algorithm (Kosorukoff, 1998) uses both human-based selection and three types of human-based innovation (contributing new content, mutation, and recombination). Thus, all operators of a typical genetic algorithm are outsourced to humans (hence the origin of human-based). Examples are Free Knowledge Exchange (FKE), Knowledge iN, and Yahoo! Answers all implement collaborative problem-solving.

• (HH₁) 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.

• (HC) Computerized tests. A computer generates a problem and presents it to evaluate a user. For example CAPTCHA, a reverse Turing test, tells human users from computer programs by presenting a problem that is supposedly easy for a human and difficult for a computer (Lillibridge 1998). Unfortunately, the results obtained from humans are usually discarded. This makes it an example of wasting human computation.

• (HC) Interactive online guessing games: These are programs that extract knowledge from people in an entertaining way (Burgener, 1999; von Ahn 2003).

[edit] Incentives to participation

In different human-based computation projects people are motivated by one or more of the following.

• Receiving a fair share of the result
• Esthetic satisfaction
• Curiousity, desire to test if it works
• Volunteerism, desire to support a cause of the project
• Reciprocity, exchange, mutual help
• Competitive spirit of a game
• Desire to communicate and share knowledge
• Desire to share a user innovation to see if someone else can improve on it
• Desire to game the system and influence the final result

Many projects had explored various combinations of these incentives. See more information about motivation of participants in these projects in Kosorukoff (2000) and von Hippel (2005).

[edit] Human-based computation as a form of social organization

Viewed as a form of social organization, human-based computation often surprisingly turns out to be a more robust and productive than traditional organizations. 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. Most people, unlike mechanical parts, find it difficult to adapt to some fixed roles that best fit the organization. Evolutionary human-computation projects offer a natural solution to this problem. They adapt organizational structure to human spontaneity, accommodate human mistakes and creativity, and utilize both in a constructive way. This leaves their participants free from obligations without endangering the functionality of the whole, making people happier (Kosorukoff and Goldberg, 2001). There are still some challenging research problems that need to be solved before we can realize the full potential of this idea.

[edit] See also

• Social software
• Social computing
• Social organization
• Symbiotic intelligence
• Collaborative intelligence
• Human computer

[edit] References

1. Caldwell, C. and Johnston V. S. (1991), 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.416-421, July 1991. (US Patent 5,375,195 filed 1992.06.29)
2. Sims, K. (1991) Artificial Evolution for Computer Graphics, Computer Graphics, 25(4) (SIGGRAPH'91), 319-328 (US Patent 6,088,510 filed 1992.07.02)
3. Herdy, M. (1996) Evolution strategies with subjective selection. In Parallel Problem Solving from Nature, PPSN IV, Volume 1141 of LNCS (pp. 22-31)
4. Unemi, T. (1998) A Design of multi-field user interface for simulated breeding, Proceedings of the Third Asian Fuzzy and Intelligent System Symposium, 489-494
5. Kosorukoff (1998) Alex Kosorukoff, Free Knowledge Exchange, human-based genetic algorithm on the web archive description
6. Lillibridge et al (1998) Method for selectively restricting access to computer systems, US Patent 6,195,698
7. Burgener (1999) Twenty questions: the neural-net on the Internet archive website
8. Kosorukoff, A. (2000) Social classification structures. Optimal decision making in an organization, Genetic and Evolutionary Computation Conference, GECCO-2000, Late breaking papers, 175--178 online
9. Kosorukoff, A. (2000) Human-based genetic algorithm online
10. Cunningham, Ward and Leuf, Bo (2001): The Wiki Way. Quick Collaboration on the Web. Addison-Wesley, ISBN 0-201-71499-X.
11. Hideyuki Takagi (2001), Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation, Proceedings of the IEEE, vol.89, no. 9, pp. 1275-1296
12. Kosorukoff, A (2001), Human-based Genetic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2001, 3464-3469
13. Kosorukoff, A. & Goldberg, D. E. (2001) Genetic algorithms for social innovation and creativity (Illigal report No 2001005). Urbana, IL: University of Illinois at Urbana-Champaign online
14. Kosorukoff, A, Goldberg D. E. (2002), Genetic algorithm as a form of organization, Proceedings of Genetic and Evolutionary Computation Conference, GECCO-2002, pp 965-972
15. von Ahn, L et al (2003) CAPTCHA: Using Hard AI Problems for Security In Eurocrypt 2003
16. von Ahn, L (2003) Method for labeling images through a computer game US Patent Application 10/875913
17. von Ahn, L, Dabbish, L (2004) Labeling Images with a Computer Game In ACM CHI 2004 online
18. von Hippel, E. (2005) Democratizing Innovation, MIT Press online
19. Gentry, C et al (2005) Secure Distributed Human Computation In Ninth International Conference on Financial Cryptography and Data Security FC'2005 online
20. von Ahn, L et al (2006) Verbosity: A Game for Collecting Common-Sense Facts, ACM CHI Notes 2006 online
21. von Ahn, L at al (2006) Improving Accessibility of the Web with a Computer Game, ACM CHI Notes 2006 online

[edit] External links

1. Human computation, a talk by Luis von Ahn