Editing Autonomic computing (section)

==Description==
The AC system concept is designed to make adaptive decisions, using high-level policies. It will constantly check and optimize its status and automatically adapt itself to changing conditions.  An autonomic computing framework is composed of autonomic [[Software component|component]]s (AC) interacting with each other. An AC can be modeled in terms of two main control schemes (local and global) with [[sensor]]s (for [[self-monitoring]]), [[Actuator|effectors]] (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness. This architecture is sometimes referred to as Monitor-Analyze-Plan-Execute (MAPE).

Driven by such vision, a variety of architectural frameworks based on "[[homoeostasis|self-regulating]]" autonomic components has been recently proposed. A similar trend has recently characterized significant research in the area of [[multi-agent system]]s. However, most of these approaches are typically conceived with centralized or cluster-based [[Server (computing)|server]] architectures in mind and mostly address the need of reducing management costs rather than the need of enabling complex software systems or providing innovative services. Some [[#Autonomic systems|autonomic systems]] involve mobile agents interacting via loosely coupled communication mechanisms.<ref>{{cite book|last=Padovitz|first=Amir|author2=Arkady Zaslavsky|author3= Seng W. Loke|chapter=Awareness and agility for autonomic distributed systems: Platform-independent and publish-subscribe event-based communication for mobile agents |title=14th International Workshop on Database and Expert Systems Applications, 2003. Proceedings. |year=2003|pages=669–673|url=http://www.computer.org/csdl/proceedings/dexa/2003/1993/00/19930669-abs.html|doi=10.1109/DEXA.2003.1232098|isbn=978-0-7695-1993-7|s2cid=15846232}}</ref>

''Autonomy-oriented computation'' is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating [[social animals]]' collective behaviours to solve difficult computational problems. For example, [[ant colony optimization]] could be studied in this paradigm.<ref>{{citation|doi=10.1007/978-3-540-25928-2_13|isbn= 978-3-540-22477-8|chapter=From Individual Based Modeling to Autonomy Oriented Computation|title=Agents and Computational Autonomy|series=Lecture Notes in Computer Science|year=2004|last1=Jin|first1=Xiaolong|last2=Liu|first2=Jiming|volume=2969|pages=151}}</ref>