Static knowledge about gait and anatomy is represented in frames and dynamic evaluation strategies are represented in frames and metarules. Initial results are described by Dzierzanowski et al. (Dzierzanowski et al., 1983). We have completed several expert systems for electroencephalogram evaluation (Jagannathan, et al., 1981, 1982) (Bourne et al., A rule-based consultant system has been implemented for advising physicians about the prescription of initial dialysis therapies (Schaffer et al, 1983). This system is now in use at the Dialysis Clinics, Inc., Nashville, Tennessee. This system is now being expanded into a community of simulated consultative experts that provide advice about pharmacology, cardiovascular problems, nutrition and other problems Personnel: J. D. Schaffer, J. Cavaedes, J Bourne This project is devoted to building a complete system that assists the electromyogram [EMG] reader.

For more detailed information about any of MITRE's projects please contact Joseph Katz (KATZQMITRE-Bedford) or Richard Brown CLINUS!BROWN@MITRE-Bedford) at the Bedford center or Peter 6onasso (BONASSOQMITRE) at the Washington center Subsequently, Rome Air Development Center took over support of the project and continues to fund part of our AI research effort. MITRE's current research is summarized below. The Bedford center is supported by 15 Symbolics Lisp machines netted to two Vax-780 file servers, while the Washington center is supported by both a classified and an unclassified facility, with 2 Lambdas and 2 Symbolics Lisp machines respectively netted to Vax-780 file servers. Both centers support creative groups of people who generate exciting new ideas. Planning MITRE Bedford AI Programs and Reasoning Research into planning and reasoning started with the development of the KNOBS system.

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.