Design Principles for Autonomous Agents - Artificial Intelligence

Pfeifer and Scheier propose several design principles for autonomous agents:

Design Pfeifer and Scheier’s design principles for autonomous agents.

Design Meta-Principle: The ‘three constituents principle’.

This first principle is classed as a meta-principle as it defines the context governing the other principles. It states that the design of autonomous agents involves three constituents:

  1. the ecological niche;
  2. the desired behaviours and tasks;
  3. the agent itself. The ‘task environment’ covers 1 and 2 together.

Design Principle 1: The ‘complete-agent principle’.

Agents must be complete: autonomous; self-sufficient; embodied; and situated.

Design Principle 2: The ‘principle of parallel, loosely coupled processes’.

Intelligence is emergent from agent-environment interaction through parallel, loosely coupled processes connected to the sensory-motor mechanisms.

Design Principle 3: The ‘principle of sensory-motor co-ordination’.

All intelligent behaviour (e.g. perception, categorization, memory) is a result of sensory-motor coordination that structures the sensory input.

Design Principle 4: The ‘principle of cheap designs’.

Designs are parsimonious and exploit the physics of the ecological niche.

Design Principle 5: The ‘redundancy principle’.

Redundancy is incorporated into the agent’s design with information overlap occurring across different sensory channels.

Design Principle 6: The ‘principle of ecological balance’.

The complexity of the agent matches the complexity of the task environment. There must be a match in the complexity of sensors, motor system and neural substrate.

Design Principle 7: The ‘value principle’.

The agent has a value system that relies on mechanisms of self - supervised learning and self organisation.

These well - crafted principles have significant implications for the design of autonomous agents. To the most part, we will try to adhere to these principles when designing our own agents in these books. We will also be revisiting aspects of these principles several times throughout these books, where we will explore specific concepts such as emergence and self-organization in more depth.

However, we will slightly modify some aspects of these principles to more closely match the terminology and approach adopted in these books. Rather than make the distinction of three constituents as in the Design Meta - Principle and refer to an ‘ecological niche’, we will prefer to use just two: agents and environments. Environments are important for agents, as agent - environment interaction is necessary for complex agent behaviour. The next part of the book will explore what we mean by environments, and have a look at some environments that mirror the complexity of the real world.

In presenting solutions to problems in these books, we will stick mostly to the design principles outlined above, but with the following further design principles:

Design Principle 8:The design should be simple, and concise (the ‘Keep It Simple Stupid’ or KISS principle).

Design Principle 9:The design should be computationally efficient.

Design Principle 10:The design should be able to model as wide a range of complex agent behaviour and complex environments as possible.

The main reason for making the design simple and concise is for pedagogical reasons. However, as we will see in latter chapters, simplicity in design does not necessarily preclude complexity of agent behaviour or complexity in the environment. For example, the NetLogo programming language has a rich set of models despite most of them being restricted to a simple 2D environment used for simulation and visualisation.


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