AI applications have been deployed and used for industrial, government, and consumer purposes for many years. The experiences have been documented in IAAI conference proceedings since 1989. Over the years, the breadth of applications has expanded many times over and AI systems have become more commonplace. Indeed, AI has recently become a focal point in the industrial and consumer consciousness. This article focuses on changes in the world of computing over the last three decades that made building AI applications more feasible. We then examine lessons learned during this time and distill these lessons into succinct advice for future application builders.

Moreover, 2 keynote and 2 tutorial papers were invited. Most regular papers were presented in one of these tracks: Smart Contracts, Blockchain, and Rules, Constraint Handling Rules, Event Driven Architectures and Active Database Systems, Legal Rules and Reasoning, Rule-and Ontology-Based Data Access and Transformation, Rule Induction, and Learning. Following up on previous years, RuleML also hosted the 6th RuleML Doctoral Consortium and the 10th International Rule Challenge, which this year was dedicated to applications of rule-based reasoning, such as Rules in Retail, Rules in Tourism, Rules in Transportation, Rules in Geography, Rules in Location-Based Search, Rules in Insurance Regulation, Rules in Medicine, and Rules in Ecosystem Research. The 10th International Rule Challenge Awards went to Ingmar Dasseville, Laurent Janssens, Gerda Janssens, Jan Vanthienen, and Marc Denecker, for their paper Combining DMN and the Knowledge Base Paradigm for Flexible Decision Enactment, and Jacob Feldman for his paper What-If Analyzer for DMN-based Decision Models. As in previous years, RuleML 2016 was also a place for presentations and face-to-face meetings about rule technology standardizations, which this year Mark Your Calendars!

RADITIONAL rule based fuzzy systems encode expert opinion in the form of IF-THEN rules and optimise some performance metric (typically mean squared error in predicting a data-set) to obtain the hyper-parameters of the model (like the numeric values defining the shape of the membership functions etc.) [2]-[4]. The rule base is one of the core elements driving the model and slight change in the rule base would significantly affect the performance of the fuzzy inference system. Many automated methods have been proposed where the rule base structure and parameters can be automatically determined [5]-[7]. However interpretability of such models is an issue and various methods have been proposed to alleviate the issue [8]. In the present paper however, we are interested in the actual metric for comparison between different models having different rule bases derived from expert opinion. The comparison metric can nevertheless be embedded within an automated framework to evolve the best model if so required. The most straight forward way of comparing the fuzzy rule bases is to optimise the model parameters based on the prediction error (e.g.

Artificial Intelligence is a Science that uses Technology to Create Magic 7. Contents • What is Artificial Intelligence?

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is applied in various fields such as computer vision, speech recognition, NLP, web search, biotech, risk management, cyber security, and many others.

Once again, the RSA conference is fast approaching, and that means it's time for the latest round of security buzzword bingo. To be sure, artificial intelligence (AI) and machine learning (ML) will be everywhere at the show. You can be certain that the halls at Moscone will be packed full of vendors pitching new security offerings that claim to use AI/ML, and there will be a glut of competitive messaging at all the other security shows, as some vendors seek to further confuse the marketplace. But the fact is, what they want to sell you are merely tools that use technology loosely based on the tenets of AI/ML, but are actually nothing more than repackaged offerings that rely on glorified signature-based security strategies. The technology the majority of these vendors are developing is basically the same type of stuff that emerged in the 1970's, and that companies were clamoring about back in the 1980's – versions of'expert systems' that have not proven to be very useful in most cases, and led to the long AI/ML winter from which we have only recently emerged.

Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE--a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-theart embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.

This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuS-SIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

Nothing seems to inspire a headline in health quite like Artificial Intelligence (AI). Ever since IBM Watson moved from the Jeopardy studios to hospitals and research centres, the industry and world have been abuzz with excited optimism about the revolutionary impact AI would have on diagnosis, treatment and prevention of a myriad of diseases. One of the flagships of this brave new intelligent world was MD Anderson, the cancer centre within the University of Texas who announced in 2013 that they would be using IBM Watson to help eradicate cancer. Finally we were living in the future where technology could help solve some of the most pervasive and devastating diseases on the planet; doing what thousands of doctors, researchers and medical professionals have spent decades working to solve. AI was not only living up to the hype, it was exceeding it.