Tuesday, 2 August 2011



Emergent behaviour; the system behaves in a novel way, unlike the way it was designed to do. Such behaviours have always been viewed with a sense of mysticism. 

Here are some opinions expressed by AI researchers and system technologists;
  • Emergence is “the appearance of novel properties in whole systems” (Moravec 1988)
  • “Global functionality emerges from the parallel interaction of local behaviors” (Steels 1990)
  • “Intelligence emerges from the interaction of the components of the system” (Brooks 1991)
  • “Emergent functionality arises by virtue of interaction between components not themselves designed with the particular function in mind” (McFarland & Bosser 1993)
  • They are a consequence underlying the complexity of the world in which the robotic agent resides and the additional complexity of perceiving that world” (Arkin 1998)
  • ....the arising of novel and coherent structures, patterns and properties during the process of self-organization in complex systems(Goldstein 1999)
  • .... where the agent appears to do something fairly complex, but is really just the result of interaction between simple modules” (Murphy 2000)
  • Behaviors serve as the basic building blocks for robotic actions, and the overall behavior of the robot is emergent” (Murphy 2000)
  • “Emergence is ubiquitous” (de Haan 2007)
Steels mentions two advantages of emergent behavior when compared to directly programmed behavior;
  • No additional structure is needed inside an agent to get additional capabilities. Therefore, we do not need any special explanations on how the behavior may come about.
  • Emergent behavior tends to be more robust because it is less dependent on accurate sensing or action and because it makes less environmental assumptions.
Goldstein points out to the generic characteristics of emergence;
  • Radical novelty (features not previously observed in the system)
  • Coherence or correlation (meaning integrated wholes that maintain themselves over some period of time
  • A global or macro “level” (i.e., there is some property of “wholeness”)
  • It is the product of a dynamical process (it evolves)
  • It is “ostensive” - it can be perceived. 
Brooks demonstrates how walking can emerge from a network of rather simple reflexes with little central control. Murphy provides an excellent insight into the topic in her book, 'Introduction to AI Robotics'.
..... away from the apparent magic, such behaviours are observed due to interaction of the system components.


In mobile robotics, a well known emergent behaviour is seen when the agent  may exhibit 'wall following', when it is programmed to 'obstacle avoidance'. 

Simple experiments in Player/Stage demonstrate this;

It is also seen that other behaviours as, 'wander' and 'random walk' fails to have a 'wall following' emergence of behaviour. 

(1) Brooks, R.A. "Intelligence without reason", Computers and Thought, IJCAI-91 ; also MIT AI Lab Memo 1293, April 1991.
(2) Steels, L. "The Artificial Life Roots of Artificial Intelligence", Artificial Life, Volume 1 Issue 1-2, Fall 1993/Winter 1994.
(3) An entertaining discussion on 'Ant Bridge' by Pedro Bittencourt 
(4) de Haan, J. "How emergence arises", Ecological Complexity 3 (2006) 293 – 301
(5) Goldstein, Jeffrey (1999), "Emergence as a Construct: History and Issues", Emergence: Complexity and Organization 1 (1): 49–72
(6) Youtube videos, 'Emergence - Complexity from Simplicity, Order from Chaos'; Part-1 & Part-2
(7) Brooks, R.A. "A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network",  MIT AI Lab Memo 1091, February 1989
(8) Ackerman, E. "Swarmanoid Robot Teams Up with Itself to Steal Your Book" - article in IEEE Spectrum, Aug 2011
(9) Wikipedia page on Emergence

1 comment:

Luke Dunn said...

I'd certainly like to see a proper "hard" maze that would engage a human, and observe the solution your system finds. I bet it could do it since wall following will eventually solve any non-closed maze, nice to have a demo though..hint hint...