To understand the possibilities that artificial intelligence (AI) can provide, it helps to understand how it functions, and that means first understanding our own ways of thinking and learning. As humans we think through what is called cognition, which is a fancy word for thought process or the action of acquiring knowledge and understanding through thought and experience.
The way we learn is through either observational or associative means. Observational learning is watching others behavior, such as watching someone ride a bike. You learn from watching how they pedal and steer along the way. Associative learning, on the other hand, is learning by establishing connections between events. You know you will hear thunder when you see a lightning strike.
So, you and I make decisions based on thought and learning constructed on observational reasoning as well as associative pattern recognition. We also sometimes make bad decisions which we can learn from to make us smarter the next time around. So, our thought process gets smarter the more we learn.
AI, the ability of a computer program or a machine to think and learn, mimics that human cognition. If a machine can acquire knowledge and understand or recognize it, then it too can start to make informed decisions and present them to us for our final evaluation. AI is really a decision support system (DSS) that helps us make a better decision faster than we could have otherwise made. Let’s take a look at two different types of AI systems and how they can help improve our planning methods for better project outcomes.
Expert (Knowledge-Based) AI Systems
Knowledge-based or expert systems (ES) came into their own as computing power expanded in the 1990s. An ES 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 subset of what it believes is the answer. In general, the more rules that can be used to answer the question, the stronger the probability a correct answer will be outputted. For example, if I wanted to determine a type of two-legged animal, simply querying “IF number of legs = 2” doesn’t narrow down our search enough to give us a useful answer since there are a large number of animals with two legs. Combine this with an additional set of questions relating to height, weight, habitat, pouch, etc. and we can quickly deduce a more reasonable answer.
Expert systems contain two key elements: a knowledge base and an inference engine. In context of a project planning tool, the knowledge base would contain data pertaining to activities and their durations for different types of projects. The inference engine is then responsible for trying to return a subset of this knowledge base back to the planner based on the question they may ask, such as “What activities should I include in the engineering scope of my hospital project?”
Neural Networks
A neural network (NN) tries to simulate the way a brain processes, learns and remembers information. Learning from experience it looks for similarities in the information that it is provided, as well as in previous data and then makes a decision based on that process – it looks for patterns. This pattern matching is called machine learning – you must teach a NN what is a match and what is not. Feed-in enough examples of characteristics of an alive human (breathing, pulse, eye movement) and a neural network will start to establish a pattern as to whether those inputs drive towards a correct diagnosis of “alive or dead?”
There are various forms of machine learning in a neural network including:
- Supervised. For example, feed-in an activity that has zero total float and tell the NN that the activity is ‘on the critical path’. After feeding in enough of these activities, it will establish a pattern that matches zero float activities to critical path activities.
- Unsupervised. Feed-in activities but don’t tell the NN which are on the critical path or not and let the NN try and categorize the activities based on its various attributes (e.g., total float). In this instance, the NN will perhaps group into zero and non-zero float without knowing this relates to a critical path – it simply groups activities together.
- Reinforcement. This is teaching through reward, like teaching a dog good behavior by offering a treat. With regards to our planning software examples, perhaps the license cost of the software should automatically go up or down depending on how good the AI engine suggestions are!
Which AI is Best for Enhanced 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 upfront” 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 plan from 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 a lot of data that, even if we could get our hands on it, wouldn’t be consistent enough to actually help with pattern recognition.
Expert systems tend to excel in environments that are more sequential, logical and can be trained by rules – which might sound a lot like the CPM you’ve been working with. Neural networks pertain more to problems such as recognition through pictures e.g., project drawings and BIM.
Planning can still benefit from a neural network approach as a way to make the tool smarter. As mentioned, expert systems can get smarter, but they need to be trained. 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, the final answer on which systems is best for project planning would be “both.” You want 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.
This is why you want to employ scheduling and risk solutions that are really knowledge-driven planning tools. They make the process of building a CPM schedule faster and the resulting plans more accurate through a combination of AI as well as what we call HI or human intelligence.
By utilizing the right type of machine learning, the suggestions the AI engine makes for us will actually evolve and get smarter over time. This means that our decisions will get smarter over time as well, allowing us to harness the true promise of construction technology on our terms, serving our project needs and goals for better project outcomes, more often.
To find out more about how you can gain more confidence that you’re handing over to an owner a project with documentable structural integrity, schedule an InEight demo today.
