Generally, business rules are modeled by domain experts and operations teams in decision automation scenarios. But what if you see a behaviour in your operational system from your customers, clients, etc. but you don't know the business rules? Business rule mining is the ability of a system allowing you to extract rules from data. In this process, you connect the data FlexRule Designer and it will analyze the data and extract the rules from it. FlexRule Designer will take it one step further and prepare and model a Decision Table and Fact Concept related to those rules automatically and will add them to the project.

One of the main weaknesses of current chatbots or dialogue systems is that they do not learn online during conversations after they are deployed. This is a major loss of opportunity. Clearly, each human user has a great deal of knowledge about the world that may be useful to others. If a chatbot can learn from their users during chatting, it will greatly expand its knowledge base and serve its users better. This paper proposes to build such a learning capability in a rule-based chatbot so that it can continuously acquire new knowledge in its chatting with users. This work is useful because many real-life deployed chatbots are rule-based.

Chinese state media with optimistic tone after Biden win. In a detailed policy document, President Trump's State Department is warning the incoming administration that it must address a Chinese government intent on displacing the United States as the world's foremost power. "The Trump administration achieved a fundamental break with the conventional wisdom. It concluded that the CCP's [Chinese Communist Party's] resolute conduct and self-professed goals require the United States and other countries to revise assumptions and develop a new strategic doctrine to address the primacy and magnitude of the China challenge," according to the document. The State Department's policy planning office, often called the department's "think tank," wrote the report as a long-term document to "sketch a framework for the fashioning of sturdy policies that stand above bureaucratic squabbles and interagency turf battles and transcend short-term election cycles."

How do AI models compare to rules-based decisioning? Brighterion shows how its AI models outperform and gives client metrics.

Money laundering is the crucial mechanism utilized by criminals to inject proceeds of crime to the financial system. The primary responsibility of the detection of suspicious activity related to money laundering is with the financial institutions. Most of the current systems in these institutions are rule-based and ineffective. The available data science-based anti-money laundering (AML) models in order to replace the existing rule-based systems work on customer relationship management (CRM) features and time characteristics of transaction behaviour. However, there is still a challenge on accuracy and problems around feature engineering due to thousands of possible features. Aiming to improve the detection performance of suspicious transaction monitoring systems for AML systems, in this article, we introduce a novel feature set based on time-frequency analysis, that makes use of 2-D representations of financial transactions. Random forest is utilized as a machine learning method, and simulated annealing is adopted for hyperparameter tuning. The designed algorithm is tested on real banking data, proving the efficacy of the results in practically relevant environments. It is shown that the time-frequency characteristics of suspicious and non-suspicious entities differentiate significantly, which would substantially improve the precision of data science-based transaction monitoring systems looking at only time-series transaction and CRM features.

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or fifinance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

Gaurav Shinh: Earlier, the vulnerability scan, and security patches were done through the rules-based system. These systems rely on human knowledge to define and configure the rules for both the detection and defining the defense mechanism in case of a security breach. Now, AI models have come up which identifies the pattern of attack and generate rules on the demand. This enabled companies to move to an era of'Proactive' defense rather than a reactive response-based system.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Los Angeles County Board of Supervisors on Tuesday voted on a motion to explore options for removing Sheriff Alex Villanueva, including an amendment to the state's Constitution. The 3-2 vote directs county officials to examine ways in which they could impeach Villanueva from his position, or, at least, scale back his responsibilities. One of those options proposed would amend California's Constitution to make county sheriffs be appointed, rather than elected.

For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules, especially for challenging scenarios that some new human drivers find difficult. In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments. Application of the hierarchical structure allows the various layers of the behavior planning system to be satisfied. Our algorithms can perform better than heuristic-rule-based methods for elective decisions such as when to turn left between vehicles approaching from the opposite direction or possible lane-change when approaching an intersection due to lane blockage or delay in front of the ego car. Such behavior is hard to evaluate as correct or incorrect, but for some aggressive expert human drivers handle such scenarios effectively and quickly. On the other hand, compared to traditional RL methods, our algorithm is more sample-efficient, due to the use of a hybrid reward mechanism and heuristic exploration during the training process. The results also show that the proposed method converges to an optimal policy faster than traditional RL methods.

End-to-end models in NLP rarely encode external world knowledge about length of time. We introduce two effective models for duration prediction, which incorporate external knowledge by reading temporal-related news sentences (time-aware pre-training). Specifically, one model predicts the range/unit where the duration value falls in (R-pred); and the other predicts the exact duration value E-pred. Our best model -- E-pred, substantially outperforms previous work, and captures duration information more accurately than R-pred. We also demonstrate our models are capable of duration prediction in the unsupervised setting, outperforming the baselines.