Automating Scheduling and Resource Allocation Decisions

Decision Optimization frequently deals with scheduling and resource allocation problems. One of the best-known software package for modeling and solving scheduling problems was ILOG Scheduler. This month IBM published a very detailed article “20+ years of scheduling with constraints at IBM/ILOG” that describes the latest IBM ILOG CP Optimizer for scheduling. You also may want to learn how to use DMN-like decision tables for “Modeling Decisions for Scheduling and Resource Allocation Problems“.

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Building Stateful Decision Services

Sometimes a decision service needs to consider information or context from previous invocations of the service. For example, you might want to award a customer discount if they purchased more than two items in one week. The information you keep about each invocation is called the state, and a decision service that uses state in its business logic is stateful. Nigel Crowther, our presenter at DecisionCAMP-2017, wrote an article that discusses three ways you can build stateful decision services with IBM ODM.

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A Path to Common Sense AI?

Our recent post talks about Paul Allen’s intention to teach computers common sense. This LinkedIn’s post is attempting to define a path for common sense reasoning (click on the image): “For computers to operate at the “common sense” level, they are required to resolve (1) common sense reasoning from logic, (2) logic from available knowledge, (3) knowledge from available information, and (4) information from available data.

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Decision Management and Semantic Reasoning

DM+SWThis September DecisionCAMP and RuleML+RR will be co-located again for the third time during the Logic for AI 2018 summit in Luxembourg. These two events represent two different but closely related fields of the knowledge representation movement: Business Rules & Decisions Management and Semantic Reasoning. In this post I want to talk about relationships between these two fields and the events.” You may read the entire article just posted by Jacob Feldman at the RuleML Blog.

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Assessing Cardiovascular Risk Factors with Computer Vision

Google Brain Team: “Recently, we’ve seen many examples of how deep learning techniques can help to increase the accuracy of diagnoses for medical imaging, especially for diabetic eye disease. In “Prediction of Cardiovascular (CV) Risk Factors from Retinal Fundus Photographs via Deep Learning” we show that in addition to detecting eye disease, images of the eye can very accurately predict other indicators of CV health. This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images.Continue reading

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The Usefulness of Imperfection

ImperfectToday Tallys Yunes shared his thoughts about Theory versus Practice in creating real-world decision models. A few quotes: “models aren’t perfect, and that’s perfectly OK. There’s a reason why business analytics is known as “the science of better” rather than “the science of provably optimal.” More often than not, it is impossible to capture all nuances of a real-life problem into a mathematical model. Therefore, solutions produced by such a model are to be taken with a grain of salt and cautious optimism.Continue reading

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Decision Management Most Influential People

This year will try to determine the most influential people in Decision Management technology in 2018. How will we decide whom to include in the DM Most Influential List? Go to this page and nominate your candidates  to the DM Most Influential List which will be finalized during DecisionCAMP in September.

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