A simple guide to Integrative AI
This page is meant to be an accessible entry point to what is meant by “Integrative AI”, and to the resources on Integrative AI that are available in the AI4EU AI on-demand platform. This guide is part of the broader AI4EU scientific vision on “Human-centered AI”, available here.
What is “Integrative AI”
Consider the problem of planning maintenance activities for a fleet of industrial machines: this task requires paying attention to minute signs of stressed components, so as to best schedule repair operations. Predictive approaches based on Deep Learning and vast amounts of data can be very accurate for this kind of problems, at the cost of being opaque. However, there is a different possible approach: mathematical formulas that describe the system as precisely as possible, using statistics and decades of empirical knowledge collected in the field.
As in this example, it often seems inevitable to make a choice between more understandable and controllable methods, and the power of learning in AI. But what if this were not strictly necessary? What if we could have accurate and understandable predictions? What if we could take advantage of both huge amounts of data and human-designed models and rules?
The goal of Integrative AI is to combine AI methods to improve beyond their individual strengths and to compensate for their individual weaknesses.
Here are a few problems arising in human-centred AI that are likely to benefit from an integration of symbolic and sub-symbolic AI approaches:
- Consider a neural network that controls traffic lights. We might want to ensure that this system never gives two crossing lanes green light at the same time and that each lane eventually gets green light in a given time span. An integrative approach, in this case, would enable using (e.g) Reinforcement Learning to provide adaptability, while safety and fairness constraints can be handled via First-Order Logic, or Answer Set Programming, or Constraint Programming.
- Consider navigating a maze that has obstacles cluttering the floor while carrying a stack of items that is sensitive to sudden movements. Tackling this problem requires a combination of spatial and temporal reasoning, from the low level of individual motions to the high level of devising an abstract plan, and rather different techniques are best suited to deal with the different levels.
- Consider a system based on Deep Learning that matches job opportunities and potential candidates. A user of the system will care about matching quality (including its fairness), but also about the motivations for its behavior. Explaining decisions may become easier if expert-designed knowledge can be injected into (say) a Deep Network, or if a Deep Network can be manipulated via symbolic reasoning techniques.
- Consider an epidemic control problem, where we need to choose how to best contain the spread of some disease. Simulation models are an effective tool to assess the efficacy of containment measures, but evaluating all possible combinations of measures takes prohibitively long. We could, however, use machine learning (ML) to approximate the behavior of the simulator, and then use optimization methods (e.g. Mixed Integer Linear Programming) to quickly find the best combination.
In general, Integrative AI consists of approaches that combine AI systems based on heterogeneous formalisms and algorithms.
Note: if you want to add a software resource, data set or researcher to this document, you first need to make sure that they are available in the AI4EU platform, e.g., by publishing the software.
This document is published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). It should be cited as:
João Paulo Costeira and Pedro Lima (editors), “A simple guide to Physical AI”. Published on the AI4EU platform: http://ai4eu.eu. June 24, 2020.