This Guest paper was first published on the PlanningPlanet blog on January 12, 2018 and is copyright to Dr. Dan Patterson, PMP, 2018
Published here
April 2018.

Introduction | What Exactly is Artificial Intelligence?
The Problem with Project Planning Today | AI Categories | Current Approaches to ANI
Neural Networks | Which AI Approach is Best for Helping with Project Planning?
Should We Embrace or Avoid AI in Planning?

Which AI Approach is Best for Helping with Project Planning?

Unlike neural networks, expert systems do not require up-front learning, nor do they necessarily require large amounts of data to be effective. Yes, expert systems can and do absolutely learn and get smarter over time (by adjusting or adding rules in the inference engine) but they have the benefit of not needing to be 'trained up front' in order to function correctly.

Capturing planning knowledge can be a daunting task and arguably very specific and unique to individual organizations. If all organizations planned to use the same knowledge, e.g., standard sub-nets, then we could simply put our heads together as an industry and establish a global 'planning bible' to which we could all subscribe. This of course isn't the case and so for a neural network to be effective in helping us in project planning, we would need to mine an awful lot of data. And, even if we could get our hands on sufficient data, it probably would not be consistent enough to actually help with pattern recognition.

Neural networks have been described as black boxes — you feed in inputs, they establish algorithms based on learned patterns and then spit out an answer. The problem is, they don't tell you why because neural networks don't understand context! I honestly don't think that as a diligent planning community, we should rely on a system that doesn't have understanding, or even worse, cannot explain why a tool comes up with a given answer.

The point is that a response like: "I'm pretty certain you need these activities just because I know" is not as useful as: "you need these activities based on previous projects X, Y, Z, given your currently defined scope and the phase of the project you are currently in".

Expert systems tend to excel in environments that are more sequential, logical and can be 'tamed' by rules. Doesn't that remind you of a CPM network?! Neural networks pertain more to problems such as recognition through pictures such as project drawings and BIM.[1]

Where I do believe a neural network approach is useful in a planning tool is in making the tool smarter. As mentioned, expert systems can get smarter but they need to be trained. For example, if we can track a planner's reaction to suggestions made by our expert system, then those reactions can be used to potentially adjust the weights we give to the various attributes in our expert system.

So, referring back to our original definition of AI, for a useful project-planning tool, we need an expert system to think and use a neural network to learn. Combine these two and we have at our disposal, an incredibly powerful planning aid. That is exactly what we have developed at BASIS.

Neural Networks  Neural Networks

1. BIM (Building Information Modeling) is a digital representation of physical and functional characteristics of a facility. A BIM is a shared knowledge resource for information about a facility forming a reliable basis for decisions during its life span: defined as existing from earliest conception to demolition.
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