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?

Current Approaches to ANI

Today, our implementation of ANI can be loosely classified into two categories: expert systems and neural networks.

Expert (Knowledge-Based) Systems (ES)

Originally developed for use in the 1980's, expert (or knowledge-based) systems really came into their own as computing power got strong enough in the 1990's. An expert system is a program running on a computer that uses a set of rules to answer a question (typically in the form of IF…THEN).

When asked a question, an ES will filter a set of data, based on rules, to establish a sub-set of what it believes is the answer.

As a general rule, the more rules that can be used to answer the question, the stronger the chance that a correct answer will be given. When determining what type of animal, simply running "IF number_of_legs = 2" doesn't narrow down our search enough to give us a useful answer given the large number of animals with 2 legs. String this to an additional set of questions relating to height, weight, habitat, pouch, etc. and we can quickly deduce a reasonable answer.

An expert system comprises a knowledge base and inference engine. For a project-planning tool, the Knowledge Base may contain data pertaining to activities and their durations for different types of projects. The inference engine is then responsible for trying to return a sub-set of this knowledge base back to the planner based on the question they may ask. For example, what activities should I include for my engineering scope of my hospital project? The relationship between computer and operator is shown in Figure 2.

Figure 2: Interaction
Figure 2: Interaction between Expert System and Human

In this example, it is also useful to understand how confident the computer is that the returned suggestion is correct. This is where the likes of fuzzy logic come into play. Rather than returning a definitive list of activities, the AI engine should return a sub-set of activities with associated degrees of confidence about their relevance.

AI Categories  AI Categories

Home | Issacons | PM Glossary | Papers & Books | Max's Musings
Guest Articles | Contact Info | Search My Site | Site Map | Top of Page