Understanding and Measuring Collective Intelligence Across Different Cognitive Systems: An Information-Theoretic Approach

Abstract

We develop the idea of collective intelligence by analysing a range of factors hindering the effectiveness of interactive cognitive agents. We give insights into how to explore the potential of collectives across different cognitive systems (human, animal and machine) and research areas. The endeavour is to bridge the different research disciplines in which collective intelligence might occur and apply the studies of intelligence in AI to other fields, thereby cross-fertilising diverse areas of study ranging from business and management to social sciences and fundamental biology.

Sports Recommender Systems: Overview and Research Issues

“…Following the concepts of collective intelligence, such basic approaches to identifying teams can be extended with regard to various dimensions [27]. For example, team recommendations could be determined not just for a single competition but also for a series of competitions which makes it important to also take into account the physical load of individual players (e.g., in the context of football world championships).…”

Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists

“…For example, Fister et al ( 2015 ) discussed how nature-inspired AI algorithms can be used to investigate unsolved research problems regarding safe and effective training plans. Their approach (Fister et al, 2015 ) relies on the notion of artificial collective intelligence (Chmait et al, 2016 ; Chmait, 2017 ) and the adaptability of algorithms to adapt to a changing environment. The authors show how such algorithms can be used to develop an artificial trainer to recommend athletes with an informed training strategy after taking into consideration various factors related to the athlete's physique and readiness.…”

Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group

“…Nader Chmait [14,17,18] provided an information-theoretic solution and at first time quantified and analyzed the impact of communication and observation abilities on the intelligence of homogeneous multi-agent system.…”

“…And an agents intelligence is measured as the ordinal of the most difficult set of environments it can pass. Chmait et al [14] proposed a metric considered universal and appropriate to empirically measure the intelligence level of different agents or groups. J Hernndez-Orallo [15] presented a way to estimate the difficulty and discriminating power of any task instance.…”