COLLABORATIVE INTELLIGENCE IN DECENTRALIZED ENVIRONMENTS
Abstract
In a dynamic decentralized computing environment, each entity (such as process, component, database, knowledge-base, and so on.), possesses various types of intelligence, such as data, information, and knowledge, and works with a specified level of autonomy. Nevertheless, the intelligences of individual entities or direct collectives of intelligences are insufficient and inadequate to accomplish the purposes of both the individual entity and/or the collective. As a result, organizations participate in collaborative efforts with the goal of sharing their intelligence resources. The collaborative strategy is motivated by a shared objective to accomplish mutual goals while also safeguarding the in interests of each individual organization. Collaborative intelligence, also known as the collaborative approach, is the outcome of the cooperation between scattered intelligences in various companies.
Collaborative intelligence is a new form of intelligence that emerges from the dispersion of intelligences in decentralized environments and refers to the emergence of intelligent behaviour from individual entities in contexts that are frequently observable and supportive. Collaborative intelligence is primarily founded on the concept that the collaborative intelligence of a group is greater than the combined intelligence of its individual members.
While collaboration and intelligence are both crucial elements of collaborative intelligence, the majority of research has generally concentrated on collaboration and on intelligence as a discernible behaviour of an entity rather than an intelligence the entity has. Therefore, it is crucial to develop a deeper understanding of how intelligence facilitates the development of collaborative intelligence that emerges from numerous distributed intelligences focused on the same domain of interest.
The aim of this work is to develop a new framework for collaborative intelligence by leveraging diverse and distributed intelligences, rather than relying on pre-established designs. This involves exploring the core nature of intelligence and developing a new paradigm. This new model of intelligence will be developed on the premise of an ontological view.
Furthermore, alongside the utilization of a new intelligence model for the advancement of collaborative intelligence, the approach of semantic integration will also be applied to construct a framework for collaborative intelligence within a decentralized environment.
This work relies not just on robust and sold theoretical foundations but also features a practical example with design directives and implementation guidelines, highlighting its practicality and efficacy in real-life situations and application domains.