We describe a program for verifying that a set of rules in an expert system comprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOCIN System, a rule-based consultant for clinical oncology The stylized format of ONCOCIN's I ules has allowed the automatic detection of a number of common errors as the knowledge base has been developed This capability suggests a general mechanism for correcting many problems with knowledge base completeness and consistency before they can cause pel fol mancc errors THI? BUILDERS FAKNOWI,EDGE-BASED cxpertsystern must ensure t,hat, t.he system will give its users accurate advice or correct solutions to t,heir problems. The process of verifying that a system is accurate and reliable has two distinct components: checking t,hat the knowledge base contains all necessary information and verifying that the program can interpret, and apply this information correctly. This process involves testing and refining the system's knowledge in order t,o discover and correct a variet.y of errors that, can arise during the process of transferring expertise from a human expert, to a computer syst,em. In this paper, we discuss some common problems in knowledge acquisition and debugging, and describe an aut,omxt,ed assistant for checking t,he completeness and consistency of the knowledge base in the ONCOCIN system (ShortJiffc, 1981).

One of the difficulties of antimicrobial prescribing lies in the necessity to sequentially adjust the treatment of a patient as new clinical data become available. The lack of specialized healthcare resources and the overwhelming amount of information to process make manual surveillance unsustainable. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. However, the highly sensitive knowledge base used by the system leads to many false alerts.

Northrop Research and Technology Center, One Research Park, Pales Wdes Peninsula, CA 90274 It, is interesting t,o note that there is no agreed upon definition of artificial intrlligence. Because government agencies ask for it, software shops claim to provide it, popular magazines and newspapers publish articles about, it, dreamers base their fant,asies on it, and pragmatists criticize and denounce it. Such a stat,c of affairs has persisted since Newell, Simon, and Shaw wrote thcif first. Not knowing exactly what we ale talking about, or expecting is typical of a new field; for example, witness the chaos that centcrcd around program verification of security rclated aspects of systems a few years ago The details are too glim to recount, in mixed company. However, artificial intelligence has been around for nearly 30 years, so one might wonder why our wheels are st,ill spinning.

This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, followup action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately. With AI, all relevant laws and regulations as well as current practices are guaranteed to be considered and followed.

A Broadened Perspective of Manufacturing: The Knowledge Network In order to form a vision and a strategy, we took a broad new look at our manufacturing business. The perspective ranged from the customer at the point of sale through point of manufacture and point of distribution and back to the customer. In 1981 DEC coined the term knowledge network to represent this notion (O'Connor 1984) (see figure 1). In many of these "pockets of expertise, " within DEC or any other manufacturing business, the expertise and the reasons for making decisions are generally undocumented or are unavailable to all the parties needing the information. Two Views of the Business Within the knowledge network two major cycles are apparent: the order-process cycle and the product life cycle The order-process cycle (see figure 2) is oriented around taking, manufacturing, delivering, and servicing an order.

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rulebased systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule-based event-processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The Ninth International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2-5. The symposium was organized by Adrian Paschke (general chair), Fariba Sadri (program cochair), Nick Bassiliades (program cochair), and Georg Gottlob program cochair).

MAgent is a research platform for many-agent reinforcement learning. Unlike previous research platforms that focus on reinforcement learning research with a single agent or only few agents, MAgent aims at supporting reinforcement learning research that scales up from hundreds to millions of agents. MAgent supports Linux and OS X running Python 2.7 or python 3. We make no assumptions about the structure of your agents. You can write rule-based algorithms or use deep learning frameworks. The training time of following tasks is about 1 day on a GTX1080-Ti card.

For those who may find awkward the reference to "half a decade" and not the "next decade" here is why: AI is evolving at such a staggering rate that it is simply not possible to foresee what it will represent in 10 years' time. As Maurice Conti (Chief Innovation Officer at Telefónica Alpha and former director at Autodesk) reminded on his intervention at TEDX in February 2017, in human history the "Hunter-Gatherer" age lasted for several million years, then the Agricultural age lasted several thousand years, the Industrial age has been around for a couple of centuries now, the Information age has merely a few decades and the AI age (although the concept was drawn in the 1950s) has in fact effectively started less than half a decade ago. It is very easy to mistake AI for RPA (Robotic Process Automation), so let's start by defining what sets them apart. RPA results from developing detail instructions that are translated into code which a computer interprets while actuating a robot. Therefore, RPA enables the integration with Mechatronics (robotic physical machines), to partially or fully automate human activities which are manual, repetitive and rule-based.

If ever there was an industry ripe for disruption it is surely the legal profession. Unlike many other sectors, however, it has tended to be a little reticent about embracing technology to innovate. After all, the traditional way of doing business for legal firms has been extremely profitable. The model typically involves a bunch of low-paid minions doing grunt work while a few partners earn eye-wateringly high sums. Moreover, many legal professionals look upon technology with fear and who could blame them when a forecast from Deloitte published last year predicted that more than 100,000 jobs in the sector could be automated within the next 20 years.

Artificial intelligence (AI) was one of 2017's hottest industry buzzwords as many have begun turning to machines to solve problems that are simply too large for humans to calculate. Once upon a time, AI was an academic pursuit -- but now it has become more affordable and attainable to pursue on a smaller scale, opening it up to use by a variety of companies for a variety of purposes. Feedzai recently told PYMNTS that Big Data paved the way for this shift, and that by 2020, U.S. companies could be saving as much as $60 billion thanks to the help of AI and machine learning. Business management consultancy Accenture expects AI to add $8.3 trillion in economic activity for the U.S. by 2035. It's clear that this trend is building some significant momentum in the payments space and adjacent industries.