Case-based reasoning (CBR) is an artificial intelligence problem solving and learning methodology that retrieves and adapts previous experiences to fit newly encountered situations. This special track, currently in its 18th year, serves as an annual forum for researchers to present and discuss developments in CBR theory and application. Mirroring the annual International Conference on Case-Based Reasoning, this year’s special track has attracted a variety of high-quality submissions that present many valuable theoretical contributions and application domains. Although the CBR special track serves an important role as a focal point for the North American CBR community, this year continues the tradition of strong international participation. We would like to thank everyone who contributed to the success of this special track, especially the authors, the program committee members, the additional reviewers, and the FLAIRS conference organizers.

Artificial Intelligence (AI) techniques are essential to the modeling of realistic behaviors for agents in simulation systems. Although Case-Based Reasoning (CBR) and Clustering techniques are being explored in the implementation of such agents in computer games, these techniques are still under-used in the implementation of simulation systems. This work approaches this gap by proposing a new CBR and clustering framework in which clustering algorithms and clustering evaluation techniques are explored in both the construction of adjusted similarity functions and the organization of sub-case bases, which are indexing components to the efficient retrieval of relevant cases from case bases so as to support the solution of new simulation problems. To evaluate this framework, a case-based algorithm was implemented to simulate the choice of military supplies to be used in artillery battery missions in virtual tactical simulations.

Recently I've been working on a customer service chatbot based on IBM Watson Assistant (formerly known as "IBM Watson Conversation Service") for a large Austrian telecommuncation provider. The chatbot was trained to answer questions on the website and to lead the user to the right website section. It currently handles 60k-80k conversations per months and covers 25% of the customer service interactions. It happened several times that minor changes in the dialog design or training caused previously working dialogs to fail -- so we were in need of regression testing. With Botium it was possible to generate test cases from the IBM Watson Assistant workspace and setup automated testing within some minutes.

Watson services on IBM Cloud are a set of REST APIs. This makes them quite simple to be used as a piece of a solution within an application. It also means they need to be integrated with various other parts of the solution to allow your users to interact with your instance of Watson. With the launch of Watson Assistant, integrating with other channels (Facebook, Slack, Intercom) has never been easier. Building a skill for Alexa is possible with Watson.

I have been writing code for a number of years now, but was finally bitten by the AI bug in 2016. A thrill of excitement ran through me as I ran demos of applications that were powered by AI. Seeing the potential and value of how AI could change our lives, I was convinced that AI was the future, only to find out later that I was wrong. AI had been a part of my life all along. It had worn several clothes like People You May Know on Facebook, autocorrect while I typed on my phone, Siri, and so many others.

While the term "machine learning" generally relates to understanding structures or patterns in data, it can also refer to a very diverse set of activities and techniques. Most of us have experienced machine learning in our everyday lives with natural language processing (Alexa, Siri), image recognition (Facebook, Pinterest), purchase recommendations (Amazon) and search optimization (Google). These approaches generally use many different types of algorithms (e.g., neural networks, decision trees, clustering, support vector machines, etc.) Industrial operations, on the other hand, need more specialized approaches that can provide actionable insights to reduce downtime as well as improve throughput, operator safety, and product quality. Whether you call it Industry 4.0 or Industrial IoT or Digital Operations, the increased access to operational data, combined with the spread of computing, connectivity, and storage, has created the perfect environment for transforming industrial operations. The real opportunity is in unlocking the value of this data.

In this paper we present the Creative Invention Benchmark (CrIB), a 2000-problem benchmark for evaluating a particular facet of computational creativity. Specifically, we address combinational p-creativity, the creativity at play when someone combines existing knowledge to achieve a solution novel to that individual.

Use streaming IoT data to uncover insights that help you better understand equipment health. Gain real-time visibility into manufacturing and supply chain processes; and monitor overall plant performance, product quality and vehicle safety issues to mitigate or avoid costly product recalls.

The graph represents a network of 3,453 Twitter users whose tweets in the requested range contained "ibm watson", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 06 May 2018 at 03:42 UTC. The requested start date was Sunday, 06 May 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 12-day, 10-hour, 28-minute period from Sunday, 22 April 2018 at 00:01 UTC to Friday, 04 May 2018 at 10:30 UTC.

Technology giants IBM and Salesforce are expanding their strategic partnership by bringing together their artificial intelligence and cloud computing platforms to help companies connect with customers and collaborate more effectively with deeper insights. Salesforce has named IBM as a preferred cloud services provider and IBM has named Salesforce as its preferred customer engagement platform for sales and service, the companies said in a release. "This expanded partnership builds on the combined power of Watson and Einstein to help enterprises make smarter business decisions," said Ginni Rometty, IBM's chairman, president and chief executive officer. Watson and Einstein are the artificial intelligence platforms of IBM and Salesforce respectively. As a part of this extended strategic partnership, IBM will build newIBM WatsonQuip Live Apps, bringing the power of Watson and Quip together.