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knowledge engineering

Artificial Intelligence Techniques Top 4 Techniques of Artificial Intelligence


Artificial Intelligence refers to machines chiefly computers working as humans. In AI, machines perform tasks like speech recognition, problem-solving and learning, etc. Machines can work and act like a human if they have enough information. So in artificial intelligence, knowledge engineering plays a vital role. The relation between objects and properties are established to implement knowledge engineering. Artificial Intelligence can be divided into different categories based on the machine's capacity to use past experiences to predict future decisions, memory, and self-awareness.

Data Interpretation Support in Rescue Operations: Application for French Firefighters Artificial Intelligence

--This work aims at developing a system that supports French firefighters in data interpretation during rescue operations. An application ontology is proposed based on existing crisis management ones and operational expertise collection. After that, a knowledge-based system will be developed and integrated in firefighters' environment. Our first studies are shown in this paper. Rescue of people consists in saving their life in case of distress situations by applying responsive operations. In France, it is defined as specific tasks to be accomplished by public services in order to ensure the safety of patients and victims by making them able to escape from dangers, securing intervention sites, providing medical help, and finally, ensuring the evacuation to an appropriate place of reception [1].

Application of Fuzzy Clustering for Text Data Dimensionality Reduction Machine Learning

Large textual corpora are often represented by the document-term frequency matrix whose elements are the frequency of terms; however, this matrix has two problems: sparsity and high dimensionality. Four dimension reduction strategies are used to address these problems. Of the four strategies, unsupervised feature transformation (UFT) is a popular and efficient strategy to map the terms to a new basis in the document-term frequency matrix. Although several UFT-based methods have been developed, fuzzy clustering has not been considered for dimensionality reduction. This research explores fuzzy clustering as a new UFT-based approach to create a lower-dimensional representation of documents. Performance of fuzzy clustering with and without using global term weighting methods is shown to exceed principal component analysis and singular value decomposition. This study also explores the effect of applying different fuzzifier values on fuzzy clustering for dimensionality reduction purpose.

Knowledge Engineering


Knowledge engineering is the process of creating rules that apply to data in order to imitate the way a human thinks and approaches problems. A task and its solution are broken down to their structure, and based on that information, AI determines how the solution was reached. Often, a library of problem-solving methods and knowledge to solve a particular set of problems is fed into a system as raw data. Then, the system can diagnose the problem and find the solution without further human input. The result can be used as a self-help troubleshooting software, or as a support module to a human agent.

Towards Interactive Training of Non-Player Characters in Video Games Artificial Intelligence

There is a high demand for high-quality Non-Player Characters (NPCs) in video games. Hand-crafting their behavior is a labor intensive and error prone engineering process with limited controls exposed to the game designers. We propose to create such NPC behaviors interactively by training an agent in the target environment using imitation learning with a human in the loop. While traditional behavior cloning may fall short of achieving the desired performance, we show that interactivity can substantially improve it with a modest amount of human efforts. The model we train is a multi-resolution ensemble of Markov models, which can be used as is or can be further "compressed" into a more compact model for inference on consumer devices. We illustrate our approach on an example in OpenAI Gym, where a human can help to quickly train an agent with only a handful of interactive demonstrations. We also outline our experiments with NPC training for a first-person shooter game currently in development.

Foundations of Digital Arch{\ae}oludology Artificial Intelligence

Digital Archaeoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines - computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. - to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project.

KNOWLEDGE GRAPHS: The role of Domain Experts - Saal


We have previously delved into detail about concept, conceptualization, and relations to build a knowledge graph. In this, we shall see how domain expertise can contribute to these vital components of the graph and its building exercise. Current knowledge engineering methodologies are analogous to software engineering approaches. Knowledge Engineers drive the knowledge graph authoring process. They are the people who know how to create formal conceptualizations of a domain but do not know the domain to be modeled.

Top Trends and Expectations from Artificial Intelligence in 2019 Analytics Insight


As the curtain rise for 2019, do expect to see major changes in how organizations use Artificial Intelligence (AI) in the new year. AI has shown immense potential to make our lives much easier, a fact which does not stop in our homes, as businesses constantly come up with new ways to use AI to engage with customers, make processes easier and pull revenues to new highs. The effectiveness and popularity of AI-powered chatbots in recent years has catapulted an increased interest in how artificial intelligence is deployed to improve the results of ad campaigns. Forrester Research says that 2019 will see the rise of new digital workers with an increased competition for data professionals with AI skills. What is next in business for AI and how can it further boost the success of businesses in the new year, here is what to expect from Artificial Intelligence in 2019.

The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning


Artificial Intelligence (AI) has entered our daily lives with a bang. From marketing to medicine, every business and industry seems to be affected. Technology companies are competing for dominance in the race to lead the market and acquire the most innovative and promising AI businesses. You may already be using AI in everyday life, with applications such as speech recognition, virtual assistance on your smartphone, the recommendation algorithms of shopping websites and music or video streaming services, or even when you visit the doctor and he compares an X-Ray or other medical images with other medical data. And then there are the terms machine learning and deep learning, which seem to confuse many people.

Why Companies That Wait to Adopt AI May Never Catch Up


Instead they are waiting for the technology to mature and for expertise in AI to become more widely available. They are planning to be "fast followers" -- a strategy that has worked with most information technologies. We think this is a bad idea. It's true that some technologies need further development, but some (like traditional machine learning) are quite mature and have been available in some form for decades. Even more recent technologies like deep learning are based on research that took place in the 1980s.