In order to explore the potential benefits of MAS to power engineering and the areas where their application may be justified, the basic concepts and approaches associated with multi-agent systems need to be understood. This leads us to a basic but essential, and unfortunately difficult, question: what is an agent?

What is an agent?

There are several understood definitions of ‘agency’ in the literature, however the definition used within the power engineering domain is that of Wooldridge‘ (1), who defines an agent as:

“a software (or hardware) entity that is situated in some environment and is able to autonomously react to changes in that environment.”

The environment may be physical (e.g., the power system), and therefore observable through sensors, or it may be the computing environment (e.g., data sources, computing resources, and other agents), observable through system calls, program invocation, and messaging. An agent may alter the environment by taking some action: either physically (such as closing a normally-open point to reconfigure a network), or otherwise (e.g., storing diagnostic information in a database for others to access).

This definition can theoretically extend to existing software and hardware systems. Arguably, under the definition above, a protection relay could be considered as an agent:

  • It is situated in its environment, i.e., the power system.
  • It reacts to changes in it environment, i.e., changes to voltage or/and current.
  • It also exhibits a degree of autonomy.

It is therefore important to distinguish between agents and multi-agent systems, and their applicability to solving engineering problems. To do this, it is necessary to extend the concept of an ‘agent’, to that of an ‘intelligent agent’

What is an intelligent agent?

An intelligent agent is one that exhibits flexible autonomy, which consists of three key characteristics:

  • Reactivity: an intelligent agent is able to react to changes in its environment in a timely fashion, and takes some action based on those changes and the function it is designed to achieve.
  • Pro-activeness: intelligent agents exhibit goal-directed behavior. Goal-directed behavior connotes that an agent will dynamically change its behavior in order to achieve its goals. For example, if an agent loses communication with another agent whose services it requires to fulfill its goals, it will search for another agent that provides the same services. Wooldridge describes this pro-activeness as an agent’s ability to “take the initiative.”
  • Social ability: intelligent agents are able to interact with other intelligent agents. Social ability connotes more than the simple passing of data between different software and hardware entities, something many traditional systems do. It connotes the ability to negotiate and interact in a cooperative manner. That ability is normally underpinned by an agent communication language (ACL), which allows agents to converse rather than simply pass data.

These three characteristics, combined with autonomy, are referred to as the weak notion of agency. It is the goal-directed behavior of individual agents and the ability to flexibly communicate and interact that set intelligent agents apart from other systems.

Multi-agent systems

A multi-agent system is simply a system comprising two or more agents or intelligent agents. A multi-agent system does not have an explicit overall system goal; instead, this is defined through the combination of multiple autonomous agents each with a specific local goal.

References

  1. M. Wooldridge, , G. Weiss, Ed., “Intelligent Agents,” in Multi-agent Systems. Cambridge, MA: MIT Press, Apr. 1999, pp. 3–51.
  2. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall, 1995.
  3. P. Maes, “Artificial life meets entertainment: Life-like autonomous agents,” Commun. ACM, vol. 38, no. 11, pp. 108–114, 1995.

Citation

If you want to reference material on this topic, please consider citing these papers as appropriate:

  • S. D. J. McArthur; E. M. Davidson; V. M. Catterson; A. L. Dimeas; N. D. Hatziargyriou; F. Ponci; T. Funabashi, “Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges”, IEEE Transactions on Power Systems, Vol. 22, No. 4, November 2007.
  • S. D. J. McArthur; E. M. Davidson; V. M. Catterson; A. L. Dimeas; N. D. Hatziargyriou; F. Ponci; T. Funabashi, “Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems”, IEEE Transactions on Power Systems, Vol. 22, No. 4, November 2007.