Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data-driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human-like choices making it possible to go beyond narrow Machine learning-based techniques and can be integrated into human decision-making. It also offers intrinsic explainability, new domain adaptability, bias free predictions, and works with datasets of all sizes. In this tutorial of type lecture style, we detail how a richer representation of causality in AI systems using a knowledge graph (KG) based approach is needed for intervention and counterfactual reasoning (Figure 1), how do we get to model-based and domain explainability, how causal representations helps in web and health care.
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Polynomial-time Implicit Learnability in SMT
To deploy knowledge-based systems in the real world, the challenge of knowledge acquisition must be addressed. Knowledge engineering by hand is a daunting task, so machine learning has been widely proposed as an alternative. However, machine learning has difficulty acquiring rules that feature the kind of exceptions that are prevalent in real-world knowledge. Moreover, it is conjectured to be impossible to reliably learn representations featuring a desirable level of expressiveness. Works by Khardon and Roth and by Juba proposed solutions to such problems by learning to reason directly, bypassing the intractable step of producing an explicit representation of the learned knowledge. These works focused on Boolean, propositional logics. In this work, we consider such implicit learning to reason for arithmetic theories, including logics considered with satisfiability modulo theory (SMT) solvers. We show that for standard fragments of linear arithmetic, we can learn to reason efficiently. These results are consequences of a more general finding: we show that there is an efficient reduction from the learning to reason problem for a logic to any sound and complete solver for that logic.
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- Award ID(s):
- 1718380
- NSF-PAR ID:
- 10187840
- Date Published:
- Journal Name:
- Proceedings of the 24th European Conference on Artificial Intelligence - ECAI 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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