Planning is a critical component of any artificial intelligence system that concerns the realization of strategies or action sequences typically for intelligent agents and autonomous robots. Given predefined parameterized actions, a planning service should accept a query with the goal and initial state to give a solution with a sequence of actions applied to environmental objects. This paper addresses the problem by providing a repository of actions generically applicable to various environmental objects based on Semantic Web technologies. Ontologies are used for asserting constraints in common sense as well as for resolving compatibilities between actions and states. Constraints are defined using Web standards such as SPARQL and SHACL to allow conditional predicates. We demonstrate the usefulness of the proposed planning domain description language with our robotics applications.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

In this paper, we introduce the increasing belief criterion in association rule mining. The criterion uses a recursive application of Bayes' theorem to compute a rule's belief. Extracted rules are required to have their belief increase with their last observation. We extend the taxonomy of association rule mining algorithms with a new branch for Bayesian rule mining~(BRM), which uses increasing belief as the rule selection criterion. In contrast, the well-established frequent association rule mining~(FRM) branch relies on the minimum-support concept to extract rules. We derive properties of the increasing belief criterion, such as the increasing belief boundary, no-prior-worries, and conjunctive premises. Subsequently, we implement a BRM algorithm using the increasing belief criterion, and illustrate its functionality in three experiments: (1)~a proof-of-concept to illustrate BRM properties, (2)~an analysis relating socioeconomic information and chemical exposure data, and (3)~mining behaviour routines in patients undergoing neurological rehabilitation. We illustrate how BRM is capable of extracting rare rules and does not suffer from support dilution. Furthermore, we show that BRM focuses on the individual event generating processes, while FRM focuses on their commonalities. We consider BRM's increasing belief as an alternative criterion to thresholds on rule support, as often applied in FRM, to determine rule usefulness.

People often talk about artificial intelligence as if it were all one thing. It's more accurate to think of AI as a collection of approaches to problems. An AI system usually combines several approaches, doing whatever produces the best results. The design of a piece of software needs to decide what problem domain it will cover. In other words, what class of problems will it deal with? The narrower the domain is, the easier the job.

These terms are heard with increasing frequency in the compliance space as solution providers seek new ways to evolve technology offerings to get ahead of compliance challenges. But does this mean that rules-based testing should be completely abandoned in favor of newer technology, or become their poor second cousin? In real-life implementations, this is simply not practical. Here are a few reasons why. Let's use a fairly simple use case to explain.

To learn more about the use of artificial intelligence at it may be applied to analyzing stocks and markets, I asked the CEO and originator of Ainstein AI about her work in this area. Suzanne Cook is a Wharton School graduate and a seven-time Institutional Investor All Star Analyst. Cook anticipates a new golden era of research - high frequency automated research - thanks to the trifecta of (1) cloud - cheaper and more accessible computing, (2) scale analytics - unifying vastly expanded data sets, and (3) autonomous pattern recognition via artificial intelligence." Here's how our conversation went: John Navin: When artificial intelligence experts talk about "natively intelligent portfolios," what exactly are they referring to? Suzanne Cook: Let's compare natively intelligent portfolios to the current portfolio offerings – not smart (analytics not built in), not in the cloud and not intuitive, as they lack visualizations.

This is a talk for people who know code, but who don't necessarily know machine learning. Learn the'new' paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will guide you through understanding many of the new concepts in machine learning that you might not be familiar with including eager mode, training loops, optimizers, and loss functions.

Fueled by mobster movies and international espionage thrillers, the phrase has a mysterious, exciting edge to it. But as is often the case, the truth is far less appealing than the glitzy Hollywood version. In reality, money laundering is an activity that traps 40.3 million people in modern slavery, fuels political unrest, and finances terrorism across the globe. Considering the consequences, it's no wonder governments enact AML regulations. These regulations have honorable and important intentions, but there's no denying the ever-evolving compliance headaches they create for financial institutions.

To learn more about the use of artificial intelligence at it may be applied to analyzing stocks and markets, I asked the CEO and originator of Ainstein AI about her work in this area. Suzanne Cook is a Wharton School graduate and a seven-time Institutional Investor All Star Analyst. Cook anticipates a new golden era of research - high frequency automated research - thanks to the trifecta of (1) cloud - cheaper and more accessible computing, (2) scale analytics - unifying vastly expanded data sets, and (3) autonomous pattern recognition via artificial intelligence." Here's how our conversation went: John Navin: When artificial intelligence experts talk about "natively intelligent portfolios," what exactly are they referring to? Suzanne Cook: Let's compare natively intelligent portfolios to the current portfolio offerings – not smart (analytics not built in), not in the cloud and not intuitive, as they lack visualizations.

Machine Learning (ML) has found its place into cybersecurity a long time ago and usage of ML has given cybersecurity teams much-needed insights into the malware network and effective ways to curb cyber attacks. Most ML-based solutions are proprietary or designed for specific feature representations. In 2017, one of the most prominent credit reporting agencies (CRA) of the United States– Equifax, suffered a huge malicious attack that led to a data breach that is famous for all the wrong reasons. Personal and sensitive data worth 148 million was lost to a data breach. Such data breach and data risks are still prevalent irrespective of the endpoint protection and other monitoring techniques deployed by enterprises worldwide.