Merovingian or a simple explanation of the Reactive Agents Concept

As I was working on my first topic for this blog – “Reactive Agents” I found myself suffering from a big, fat case of writer’s block.

I have been working with Reactive Agents for over 15 years; they form the foundation of our Manufacturing Operations Management platform. When we talk to our customers about them we often use an analogy – they’re like functional “Lego Blocks” that you can use to build manufacturing systems, but we rarely talk about what “Reactive Agents” really are and why they are the foundation for our technology. Believe me, there are a lot of reasons– developer productivity, scalability, flexibility, reuse, etc., the list goes on and on.

But I was stuck, and quickly realizing that the topic requires a good introduction for novice readers, something that won’t scare people away with overly complex theory and terminology. I needed an inspiration, something better than the quote from the first published article about Reactive Agents: “In order to build a system that is intelligent, it is necessary to have representations grounded in the physical world, such obviating the need for symbolic representations or models because the world becomes its own best model.” (Brooks, 1991).

I turned my attention to the TV, unexpectedly in about 4 to 5 clicks I got my inspiration – “Matrix Reloaded” was on, right about the time that Neo and friends walked into a club looking for the “Key Maker” (here is the clip if you need to refresh your memory http://www.youtube.com/watch?v=E_PFZ92dMys#t=2m05s)

The Merovingian talked about “cause and effect” – the philosophical concept of causality, in which an action or event will produce a certain response to the action in the form of another event. I have seen this scene numerous times before, never realizing how it is related to the products and technologies I have been working on for years.

You can’t model the world, but with reactive agents, you don’t have to. In Brooks’ words: “this hypothesis obviates the need for symbolic representations or models because the world becomes its own best model. Furthermore, this model is always kept up-to-date since the system is connected to the world via sensors and/or actuators. Hence, the reactive agents hypothesis may be stated as follows: smart systems can be developed from simple agents which do not have internal symbolic models, and whose 'smartness' derives from the emergent behavior of the interactions of the various agents.”

Cause and effect…



Stay tuned for more on Reactive Agents and how Savigent is applying this technology in manufacturing operation management systems. Upcoming posts will cover base concepts as well as the software side of the Agent-based platform.