Learning Systems Engineering

Abstract

Traditionally multi-agent learning is considered as the intersection of two subfields of artificial intelligence: multi-agent systems and machine learning. Conventional machine learning involves a single agent that is trying to maximise some utility function without any awareness of existence of other agents in the environment (Mitchell, 1997). Meanwhile, multi-agent systems consider mechanisms for the interaction of autonomous agents. Learning system is defined as a system where an agent learns to interact with other agents (e.g., Clouse, 1996; Crites & Barto, 1998; Parsons, Wooldridge & Amgoud, 2003). There are two problems that agents need to overcome in order to interact with each other to reach their individual or shared goals: since agents can be available/unavailable (i.e., they might appear and/or disappear at any time), they must be able to find each other, and they must be able to interact (Jennings, Sycara & Wooldridge, 1998).

System of systems engineering governance framework for digital transformation: A case study of an Australian large government agency

“…The first constituent system, is the cognitive system, also referred to as an individual mental system, consists of multiple interrelated assumptions, beliefs, ideas, and knowledge 42 . This system consists of an individual's worldviews and determines how individuals act, think and filter information and cues from the outside world.…”