Rule-Based Reasoning

The integrated use of Machine Learning (ML) and Business Rules (BR) is one of the most practical trends in the development of modern decision-making software. OpenRules is involved in this development for more than 10 years starting with our successful ML BR projects for IRS. Along with a general purpose Rule Learner, we also provide Rule Compressor, that uses ML to compress large decision tables to smaller ones. This recent presentation explains how it works. Let's consider a simple example.

It was only a few weeks ago that Microsoft announced enhancements heading to Power BI and PowerApps, its no-code business analytics service and web apps design platforms, respectively. But that didn't stop it from unveiling yet another set of features during the Microsoft Business Applications Summit in Atlanta, Georgia this week, where the company took the wraps off a new look for Power BI and improvements in Microsoft Flow, a service which lets users create rule-based workflows that automatically trigger actions, along with improvements in Power BI and PowerApps. "We are getting tremendous feedback and energy from our customers and developers. That feedback helps us develop products that are tailored to their needs," said Microsoft corporate vice president James Phillips. "From there we get to see them innovate and thrive. It's been amazing to see us growing across the board, but there is nothing more rewarding than seeing our customers, partners, and developers in action."

Business rules work very well to represent the logic for certain domains. They work well because they result intuitive and close to the way of thinking of many types of domain experts. The reason for that it is that they permit to decompose a large problem in single components. In this article, we will discuss one specific example of an application written by using business rules. We will write the rules to decide, which email to send to the subscribers to a newsletter. We will see different types of rules and how we could express them using the Drools Rule Language.

We introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition. The problem is, given a game level as input, to generate the rules of a game that fits that level. This can be seen as the inverse of the General Video Game Level Generation problem. Conceptualizing these two problems as separate helps breaking the very hard problem of generating complete games into smaller, more manageable subproblems. The proposed framework builds on the GVGAI software and thus asks the rule generator for rules defined in the Video Game Description Language. We describe the API, and three different rule generators: a random, a constructive and a search-based generator. Early results indicate that the constructive generator generates playable and somewhat interesting game rules but has a limited expressive range, whereas the search-based generator generates remarkably diverse rulesets, but with an uneven quality.

This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set complexity and prediction accuracy. Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one. The column generation subproblem is solved using either integer programming or a heuristic optimizing the same objective. In experiments involving logistic and linear regression, the proposed methods obtain better accuracy-complexity trade-offs than existing rule ensemble algorithms. At one end of the trade-off, the methods are competitive with less interpretable benchmark models.

Knowledge representation and reasoning (KRR) is one of the key areas in artificial intelligence (AI) field. It is intended to represent the world knowledge in formal languages (e.g., Prolog, SPARQL) and then enhance the expert systems to perform querying and inference tasks. Currently, constructing large scale knowledge bases (KBs) with high quality is prohibited by the fact that the construction process requires many qualified knowledge engineers who not only understand the domain-specific knowledge but also have sufficient skills in knowledge representation. Unfortunately, qualified knowledge engineers are in short supply. Therefore, it would be very useful to build a tool that allows the user to construct and query the KB simply via text. Although there is a number of systems developed for knowledge extraction and question answering, they mainly fail in that these system don't achieve high enough accuracy whereas KRR is highly sensitive to erroneous data. In this thesis proposal, I will present Knowledge Authoring Logic Machine (KALM), a rule-based system which allows the user to author knowledge and query the KB in text. The experimental results show that KALM achieved superior accuracy in knowledge authoring and question answering as compared to the state-of-the-art systems.

Research has provided evidence that associative classification produces more accurate results compared to other classification models. The Classification Based on Association (CBA) is one of the famous Associative Classification algorithms that generates accurate classifiers. However, current association classification algorithms reside external to databases, which reduces the flexibility of enterprise analytics systems. This paper implements the CBA in Oracle database using two variant models: hardcoding the CBA in Oracle Data Mining (ODM) package and Integrating Oracle Apriori model with the Oracle Decision tree model. We compared the proposed model performance with Naive Bayes, Support Vector Machine, Random Forests, and Decision Tree over 18 datasets from UCI. Results showed that our models outperformed the original CBA model with 1 percent and is competitive to chosen classification models over benchmark datasets.

Businesses across verticals are moving from digitisation to cognification of everything. Having said that, banks and financial institutions have recognised the potentials of Artificial Intelligence (AI) to redefine their processes, products and services. With customer experience becoming vital to ensure good business, banks have been adopting AI solutions to further enhance their services what with virtual assistants and chatbots handling different customer queries. The banking industry is using AI to reimagine products, processes, strategies and the overall customer experience. Cutting edge AI research and development is transforming the sector through an automated, integrated, collaborated approach to cyber defence and helping facilitate better information sharing between security components within and across organizations.

DINARD, FRANCE - Foreign ministers of Group of Seven nations on Saturday pushed North Korea to continue denuclearization negotiations with the United States while vowing to maintain pressure on Pyongyang to encourage it to give up its nuclear weapons and ballistic missile programs. In a communique issued after a two-day meeting in Dinard, western France, the ministers also expressed serious concern about the situation in the East and South China seas -- a veiled criticism of China's militarization of outposts in disputed areas of the South China Sea and its attempts to undermine Japan's control of the Senkaku Islands in the East China Sea. The Senkakus are administered by Japan, but claimed by China and Taiwa, which call them the Diaoyu and Tiaoyutai, respectively. During the meeting, some G7 members touched on China's expanding global ambitions through its signature Belt and Road Initiative infrastructure project, a Japanese official said. But the communique makes no reference to the initiative in an apparent effort to demonstrate unity among the group.

Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level lateral decision-making and low-level rule-based trajectory modification, a safe and efficient lane change behavior can be achieved. With the setting of our state representation and reward function, the trained agent is able to take appropriate actions in a real-world-like simulator. The generated policy is evaluated on the simulator for 10 times, and the results demonstrate that the proposed rule-based DQN method outperforms the rule-based approach and the DQN method.