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• Collaboration and Stakeholder Engagement: A successful smart ecosystem requires collaboration among
              various stakeholders, including government entities, private organisations, academic institutions, and citizens.
              Engaging all relevant parties ensures that the ecosystem aligns with the needs and priorities of the community.
              Collaboration fosters innovation, knowledge sharing, and the development of solutions that address complex
              challenges effectively.
            Overall, a smart ecosystem represents a holistic approach to leveraging technology, connectivity, and data to
            create intelligent, sustainable, and efficient environments. It holds the potential to transform cities, industries,
            and communities by enabling data-driven decision-making, optimising resource usage, and enhancing the
            overall quality of life.


                         Reboot


                 1.   What do you understand by the term 'Smart Ecosystem'?




                 2.   What are the essential components of a smart ecosystem?










            NARS [Non-Axiomatic Reasoning Systems]

            NARS is based on the opinion that the essence of intelligence is the ability to adapt with insufficient knowledge
            and resources.
            Non-Axiomatic Reasoning System (NARS) is a cognitive architecture developed by Pei Wang that aims to model
            human-like reasoning and learning processes. NARS is based on the idea that intelligent systems should be able
            to reason and learn without relying on a fixed set of axioms or pre-determined knowledge.

            Traditional logic-based systems often rely on a set of axioms or initial assumptions to guide the reasoning
            process. However, NARS takes a different approach by allowing the system to generate its own knowledge
            through a combination of deductive and inductive reasoning, as well as learning from experience.

            NARS represents knowledge using a language-like formalism called “term logic.” It employs a cycle of processes,
            including attention allocation, inference, judgment, and learning, to reason about and learn from incoming
            information.  The  system  assigns  different  levels  of  attention  to  different  information,  which  influences  the
            reasoning and learning processes.
            NARS uses a non-monotonic reasoning approach, which means that its conclusions can be revised or updated
            when new information becomes available. This allows NARS to handle incomplete or uncertain information and
            make plausible inferences based on available evidence.

            One of the key features of NARS is its ability to learn from experience. It can acquire new knowledge through
            a learning mechanism called “anticipation and update.” By comparing its predictions or expectations with the
            actual outcomes of events, NARS can adjust its knowledge base and improve its future reasoning and learning
            processes.

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