A rule-based system may be viewed as consisting of three basic components: a set of rules [rule base], a data base [fact base], and an interpreter for the rules. In the simplest design, a rule … can be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.
– from The Origin of Rule-Based Systems in AI. Randall Davis and Jonathan J. King, reprinted as Ch. 2 of Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Bruce G. Buchanan and Edward H. Shortliffe (Eds.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1984.
Most insurance companies depend on human expertise and business rules-based software to protect themselves from fraud. And the drive for digital transformation and process automation means data and scenarios change faster than you can update the rules. Machine learning has the potential to allow insurers to move from the current state of "detect and react" to "predict and prevent." It excels at automating the process of taking large volumes of data, analysing multiple fraud indicators in parallel – which taken individually may often be quite normal – and finding potential fraud. Generally, there are two ways to teach or train a machine learning algorithm, which depend on the available data: supervised and unsupervised learning.
Teledata Communications, Inc. (TCI), the provider of DecisionLender 4 (DL4), a complete consumer loan origination platform, announced it has developed the industry's first AI-powered, natural language rules engine that leverages machine learning to quickly and easily create and maintain risk-based rules and lending policies. Lenders can now effortlessly create and maintain credit and lending policies; the intuitive process does not require any specialized software development skills or the addition of IT resources or third-party assistance. TCI's DecisionLender 4 implementation of Natural Language Understanding (NLU) utilizes machine learning that enables users to create rules using plain English and then convert the rule into code automatically. Any business user can now add new rules, edit existing rules and maintain risk policies. Read More: How Artificial Intelligence and Blockchain is Revolutionizing Mobile Industry in 2020?
In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities. Moreover, they are black-box models that are not easily explainable for humans. We propose DRUM, a scalable and differentiable approach for mining first-order logical rules from knowledge graphs that resolves these problems. We motivate our method by making a connection between learning confidence scores for each rule and low-rank tensor approximation.
We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-value rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency.
Lifted inference rules exploit symmetries for fast reasoning in statistical rela-tional models. Computational complexity of these rules is highly dependent onthe choice of the constraint language they operate on and therefore coming upwith the right kind of representation is critical to the success of lifted inference.In this paper, we propose a new constraint language, called setineq, which allowssubset, equality and inequality constraints, to represent substitutions over the vari-ables in the theory. Our constraint formulation is strictly more expressive thanexisting representations, yet easy to operate on. We reformulate the three mainlifting rules: decomposer, generalized binomial and the recently proposed singleoccurrence for MAP inference, to work with our constraint representation. Exper-iments on benchmark MLNs for exact and sampling based inference demonstratethe effectiveness of our approach over several other existing techniques.
This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization.
The Venture Capital Report by PwC and CB Insights revealed that funding of AI start-ups and ventures rose 72% in 2018, hitting a record of $9.3 billion. Unicorn start-ups like Paytm, OYO, and Swiggy have been investing resources to gain AI capabilities and have invested in at least one AI company. In the same year, Venture Capitals have funded AI start-ups in India with $478.38 million in 111 funding rounds. One of the reasons for this rapid growth of AI and Machine Learning technologies is the competitive edge one can get using those. It is especially the case when it comes to cost optimization and customer experience.
Recently I participated in the ThinkX program sponsored by SAP Innovation Partnership Program and Singularity University. The origins of ThinkX touch the xPrize competitions and other design events which seek to leverage exponential learning. Over the past 5 years, SAP and SU have hosted over 800 SAP executives, leaders and employees to events around the world. In this second article of a three-part series I will explore the big trends, themes and take-aways - including how they may impact industry and society in the coming years. Advances in cognitive science, particularly in areas such as Artificial Intelligence (AI) and blockchain will increase at an accelerated rate.
The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques.
Artificial intelligence is forcing industries to evolve at an incredible rate. By helping companies save time and money, AI is already revolutionizing productivity and customer service around the world. A Microsoft survey of more than 400 senior executives across eight global markets found that executives expect AI to benefit their businesses to positively impact growth, productivity, innovation, and job creation. A stunning 94% of them see AI as an important tool for solving strategic challenges. Artificial intelligence has already had a profound impact on the ways many industries do business ... [ ] every day.