In this paper, we present RALE-ACL, a communication language for case-based agents in multi-agent systems (MAS) that utilize case-based reasoning (CBR) as the main means of decision making for their agents. RALE-ACL is an accompanying approach of RALE-CBR, a methodology for construction of CBR-based approaches and systems that adds more flexibility to the classic 4R cycle of case-based reasoning. The main goal of RALE-ACL is to establish a much more CBR-compatible alternative to the KQML and FIPA-ACL-based languages, that are currently used in many multi-agent systems, but are too generic and therefore only cumbersomely usable for the specific structure and purposes of case-based agents. This paper is the final part in the trilogy about the RALE methodology.

The main objective of this research is to increase the quality of AI used in commercial RTS games, which has seen little improvement over the past decade. This objective will be addressed by investigating the use of a learning by observation, case-based reasoning agent, which can be applied to new RTS games with minimal development effort. To be successful, this agent must compare favourably with standard commercial RTS AI techniques: it must be easier to apply, have reasonable resource requirements, and produce a better player. Currently, a prototype implementation has been produced for the game StarCraft, and it has demonstrated the need for processing large sets of input data into a more concise form for use at run-time.

We propose a method for obtaining character behavior diversity based on the diversity of plans enacted by characters, and demonstrate this method in a scenario in which characters have multiple choices. Using case-based planning techniques, we reuse plans for varied character behavior, which simulate different personality traits.

Most likely, you're familiar with Watson from the IBM computer system's appearance on Jeopardy! in 2011, when it beat former champions Ken Jennings and Brad Rudder. Watson Health was supposed to change health care in a lot of important ways, by providing insight to oncologists about care for cancer patients, delivering insight to pharmaceutical companies about drug development, helping to match patients with clinical trials, and more. It sounded revolutionary, but it never really worked. Recently, Watson Health was, essentially, sold for parts: Francisco Partners, a private equity firm, bought some of Watson's data and analytics products for what Bloomberg News said was more than $1 billion. On Friday's episode of What Next: TBD, I spoke with Casey Ross, technology correspondent for Stat News, who has been covering Watson Health for years, about how Watson went from being the future of health care to being sold for scraps.

Ten years ago, IBM made a gamble. Through a monumental advertising and PR campaign, it promised that its AI technology–Watson–would transform the health care industry as we know it. A decade and billions of dollars later, Watson Health is being sold for parts. What went wrong with IBM's "moonshot?" And what does Watson's failure tell us about the promise of AI for health care?

A machine-learning expert and a psychology researcher/clinician may seem an unlikely duo. But MIT's Rosalind Picard and Massachusetts General Hospital's Paola Pedrelli are united by the belief that artificial intelligence may be able to help make mental health care more accessible to patients. In her 15 years as a clinician and researcher in psychology, Pedrelli says "it's been very, very clear that there are a number of barriers for patients with mental health disorders to accessing and receiving adequate care." Those barriers may include figuring out when and where to seek help, finding a nearby provider who is taking patients, and obtaining financial resources and transportation to attend appointments. Pedrelli is an assistant professor in psychology at the Harvard Medical School and the associate director of the Depression Clinical and Research Program at Massachusetts General Hospital (MGH).

IBM said Friday it will sell the core data assets of its Watson Health division to a San Francisco-based private equity firm, marking the staggering collapse of its ambitious artificial intelligence effort that failed to live up to its promises to transform everything from drug discovery to cancer care. Data and analytics assets held by the health business, which was not profitable, were sold to Francisco Partners as IBM seeks to refocus its business on cloud computing and AI services to help clients in multiple industries build machine learning tools and secure and manage their data. Terms of the transaction were not disclosed. Unlock this article by subscribing to STAT and enjoy your first 30 days free! STAT is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.

Health insurance companies have been looking to artificial intelligence (AI) and machine learning to identify at-risk individuals and reduce rising costs in the healthcare sphere.

Watson, a supercomputer by IBM, shot to fame in 2011 as the'brain' that beat two of the best contestants of Jeopardy! to win a million dollars. This system that combines artificial intelligence (AI) and sophisticated analytical software to answer questions was widely deployed in many industries. The supercomputer was developed in IBM's DeepQA project and was named after IBM's founder Thomas J. Watson. "You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes, make all you can. Because, remember that's where you'll find success – on the far side of failure."

Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they struggle to reason rare or emerging unseen entities. In this paper, we propose kNN-KGE, a new knowledge graph embedding approach, by linearly interpolating its entity distribution with k-nearest neighbors. We compute the nearest neighbors based on the distance in the entity embedding space from the knowledge store. Our approach can allow rare or emerging entities to be memorized explicitly rather than implicitly in model parameters. Experimental results demonstrate that our approach can improve inductive and transductive link prediction results and yield better performance for low-resource settings with only a few triples, which might be easier to reason via explicit memory.