New advances in artificial intelligence (AI) and machine learning (ML) research made by tech giants and academia have quickly made their way into businesses and business models, while even more companies are introducing established AI solutions like chatbots and virtual assistants. Following all that is happening in the dynamic world of AI is time-consuming for entrepreneurs who are busy running their own companies, so I've compiled a list of the most interesting AI trends entrepreneurs should keep an eye on in the coming year. The trend toward humanization of big data and data analytics will continue in 2018 with new advancements in natural language generation (NLG) and natural language processing (NLP). Using rule-based systems like Wordsmith by Automated Insights, media outlets and companies can already turn structured data into intelligent narratives based on natural language. Making relationships in data understandable to people beyond data science teams will further democratize AI and big data, leading to the era of automatic generation of insights.

Robotic soccer is an ideal task to demonstrate new techniques and explore new problems. Moreover, problems and solutions can easily be communicated because soccer is a well-known game. Our intention in building a robotic soccer team and participating in RoboCup-98 was, first, to demonstrate the usefulness of the self-localization methods we have developed. Second, we wanted to show that playing soccer based on an explicit world model is much more effective than other methods. Third, we intended to explore the problem of building and maintaining a global team world model.

The Electrical Systems Division at the NASA Kennedy Space Center has developed and deployed an agent-based tool to monitor the space shuttle's ground processing telemetry stream. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Efficiency and safety are improved through increased automation.

The need for online diagnostics in the electric powergeneration industry is driven by a number of significant factors . Due to the low number of new power plants being built by electric utilities, the average age of existing power plant equipment in the United States and its susceptibility to failure is increasing rapidly. Figure 1 shows the percentage of power-generation equipment over 20 years old as a function of year. Note the rapid increase of average age after 1980 and the fact that by the year 2000 fully 50 percent of all generation equipment in the United States will be over 20, the oldest average age of power plant equipment ever experienced by U.S. utilities. Thus, there is a need to know what the actual operating condition of the equipment is at all times, so that outages can be avoided by taking corrective actions at the earliest possible time and by preplanning for outages if they become necessary in order to to minimize their length.

Two topics are treated here First, we present a user model pattcrncd after the stereotype approach (Rich, 1979) This model surpasses Rich's model with respect to its greater flexibility in the construction of user profiles, and its trcat,ment of positive and negative arguments. Second, we present an inference machine This machine treats uncertain knowledge in t,he form of evidence for and against the accuracy of a proposition. Assuming a homogeneous user group, systems developers were able to design a system to perform in accordance with the requirements and capabilities assumed for a partirulal type of user (implicit user modeling). With a heterogeneous user group, this is no longer possible. Since self-assessment,s usually render a distorted picture of the user and are not expected in a real consultative dialogue, they should not be specially required in man-machine communication.

Department of Computer Science Carnegae-Mellon Unaverszty P&burg, PA 15213 Editors' Note: Many expert systems require some means of handling heuristic rules whose conclusions are less than certain Baysian techniques and other numerical scoring methods have been developed to combine and propagate certainty measures as the expert system draws inferences in solving different problems. Doyle's paper argues that it is difficult for a human expert to produce reliable probabilities or numerical scoring factors for an inference rule, and that a radically different approach to the problem should be considered He essentially suggests that the expert be encouraged to think in terms of specific instances which would conflict with the general rule and to encode this knowledge explicitly. Methodologically this seems to be very appealing, and helps to make both explicit and rigorous some of the techniques currently used by knowledge engineers whm they encode and refine the expert's knowledge We would welcome comments and criticisms of this approach from those steeped in the practical issues of constructing large rule-based expert systems. Probabilistic rules and their variants have recently supported several successful applications of expert systems, in spite of the difficulty of committing informants to particular conditional probabilities or "certainty factors," and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values Here we survey recent developments concerning reasoned assumptions which offer hope for avoiding the practical elusiveness of probabilistic rules while retaining theoretical power, for basing systems on the information unhesitatingly gained from expert informants, and reconstructing the entailed degrees of belief later @

In fact, there are enough opinions for four men. That is, the views advanced are contradictory. I agree with one of the A fifth answer is also advanced, but is immediately withdrawn. Roger Schanks, and disagree with the other three. Schank hoped that his article would start a debate on As & hank points out, this is unsatisfactory because it leads the issues he raised.

One of the major problems in developing so-called intelligent computer-aided design (CAD) systems (ten Hagen and Tomiyama 1987) is the representation of design knowledge, which is a two-part process: the representation of design objects and the representation of design processes. We believe that intelligent CAD systems will be fully realized only when these two types of representation are integrated. Progress has been made in the representation of design objects, as can be seen, for example, in geometric modeling; however, almost no significant results have been seen in the representation of design processes, which implies that we need a design theory to formalize them. According to Finger and Dixon (1989), design process models can be categorized into a descriptive model that explains how design is done, a cognitive model that explains the designer's behavior, a prescriptive model that shows how design must be done, and a computable model that expresses a method by which a computer can accomplish a task. A design theory for intelligent CAD is not useful when it is merely descriptive or cognitive; it must also be computable.

Studies in design methodology provide various structured approaches to the design process. Many books provide definitions and elaborations of the design process: In the structural engineering field, such books include Holgate (1986) and Lin and Stotesbury (1981). More generally, various design methods and techniques are described in Alexander (1964) and Jones (1970). These design methods share the characteristic of prescribing a general set of tasks to be performed by the designer. One problem with design methodologies is that such approaches prescribe what a designer should do but not how.

I should like to lodge a complaint about your editorial standards in the article "An Assessment of Tools for Building Large KB Systems," by William Mettrey, in the winter 1987 [volume 9 number As a primary architect of CRL-Ops and a former KnowledgeCraft class instructor, I had to deal with the general public's misconceptions about forward versus backward chaining systems. Mr. Mettrey's article, in my opinion, is the type which generates the confusion that forward chaining rule systems cannot "backwards chain." This nonsensical view was held by the vast majority of our customers in the KC class. The section on Rule-Based inference implies that backward chaining is done only by Prolog in KC with its statement "by contrast, Knowledge-Craft implements backward chaining by supporting a version of Prolog." Any forward chaining rules system can efficiently implement constrained backward chaining by simply using a goal structure to search for the required knowledge.