Artificial Intelligence and Collective Intelligence

The vision of artificial intelligence (AI) is often manifested through an autonomous software module (agent) in a complex and uncertain environment. The agent is capable of thinking ahead and acting for long periods of time in accordance with its goals/objectives. It is also capable of learning and refining its understanding of the world. The agent may accomplish this based on its own experience, or from the feedback provided by humans. Famous recent examples include self-driving cars (Thrun 2006) and the IBM Jeopardy player Watson (Ferrucci et al. 2010). This chapter explores the immense value of AI techniques for collective intelligence, including ways to make interactions between large numbers of humans more efficient. By defining collective intelligence as “groups of individuals acting collectively in an intelligent manner,” one soon wishes to nail down the meaning of individual. In this chapter, individuals may be software agents and/or people and the collective may consist of a mixture of both. The rise of collective intelligence allows novel possibilities of seamlessly integrating machine and human intelligence at a large scale – one of the holy grails of AI (known in the literature as mixed-initiative systems (Horvitz 2007)). Our chapter focuses on one such integration – the use of machine intelligence for the management of crowdsourcing platforms (Weld, Mausam, and Dai 2011). Crowdsourcing is a special case of collective intelligence, where a third party (called the requestor) with some internal objective solicits a group of individuals (called workers) to perform a set of inter-related tasks in service of that objective. The requestor’s objective may be expressed in the form of a utility function to be maximized. For example, a requestor might wish to obtain labels for a large set of images; in this case, her utility function might be the average quality of labels subject to a constraint that no more than $ X dollars be spent paying workers. We assume that the workers act independently, interacting only through the shared tasks. Each worker has an individual utility function, which is often different from the collective’s utility function. Furthermore, we assume that their utility functions are independent of each other. The AI subfield of multi-agent systems considers even richer models, in which individual agents may reason about the objectives of other agents, negotiate, and bargain with each other (Weiss 2013). We won’t discuss these techniques