Cognitive Artificial Intelligence Research Articles

Cognitive artificial-intelligence for doernenburg dissolved gas analysis interpretation

This paper proposes Cognitive Artificial Intelligence (CAI) method for Dissolved Gas Analysis (DGA) interpretation adopting Doernenburg Ratio method. CAI works based on Knowledge Growing System (KGS) principle and is capable of growing its own knowledge. Data are collected from sensors, but they are not the information itself, and thus, data needs to be processed to extract information. Multiple information are then fused in order to obtain new information with Degree of Certainty (DoC). The new information is used to identify faults occurred at a single observation. The proposed method is tested using the previously published dataset and compared with Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). Experiment shows CAI implementation on Doernenburg Ratio performs 115 out of 117 accurate identification, followed by Fuzzy Inference System 94.02% and ANN 78.6%. CAI works well even with small amount of data and does not require trainings.

Identifying Changes in a Student’s Mental Models and Stimulating Intrinsic Motivation for Learning During a Dialogue Regulated by the Teachback Technique: a Case Study

The aim of this paper is to show how an innovative technique can be used to introduce a method for uncovering intrinsic mechanisms that motivate changes in students’ mental models. The theoretical framework used to develop the method is based on the ONEPSY (ONtology, EPistemology and PSYchology) model. The ONEPSY model is rooted in the theoretical constructs of cognitive psychology (Johnson-Laird) and artificial intelligence on mechanistic mental models (de Kleer and Brown) as well as the psychological theories developed by Piaget and others about the construction of knowledge and cognitive mechanisms that enable us to learn and survive in the world. This method was used when regulating a dialogue between a teacher (interviewer) and a student (interviewee) and also when analysing this dialogue subsequently. Pask’s teachback technique was used to regulate the interview and showed its effectiveness for helping a student both to identify his difficulties and to be able to overcome them; it also helped him to develop the ability to build models that gradually move closer to scientific models based on intuitive reasoning (mental models). The emotions experienced by students during this process have been shown to be a decisive driving force—intrinsic motivation—for constructing and reconstructing their intuitive models and acquiring increasingly satisfactory mental models. The different regulatory processes controlled by the interviewer during the dialogue have also been outlined.

Incorporating Cognitive Artificial Intelligence Systems and Real-Time Data Analytics in Clinical Care Delivery

Incorporating Cognitive Artificial Intelligence Systems and Real-Time Data Analytics in Clinical Care Delivery

BP and Startup Beyond Limits Try To Prove That Cognitive AI Is Ready for Oil and Gas

BP has invested more than $100 million into nine different startup companies in the past 2 years—but only one of them wants to turn your brain into a piece of its software. The international major is working with the ambitiously named firm Beyond Limits on a set of artificial intelligence (AI) programs that will absorb the learnings of geologists and petroleum engineers, and then imitate their decision-making processes as they work on subsurface challenges together. Before this partnership, Beyond Limits had never been involved in solving the complexities of oil and gas, which is something that might be counted against it by venture capital or prospective upstream clients. But after BP saw what the young company was working with in its office about 10 miles north of downtown Los Angeles, it decided to become both its largest client and its largest investor by injecting it with $20 million a year ago. The attraction for BP came down to getting its hands on a strain of AI known as cognitive computing, or what Paul Stone refers to as “the pinnacle of the artificial intelligence pyramid.” “We haven’t seen that elsewhere, so we wanted to be the first to engage and see where it might go in the oil and gas industry,” said Stone, who serves as a technology director in BP’s digital innovation group. And where it lacks a prior track record in oil and gas, Beyond Limits compensates with a technology team that helped design most of the same intelligent software it is licensing from the Jet Propulsion Laboratory ( JPL), an institution run by the influential California Institute of Technology in cooperation with NASA. Similar to oil and gas explorers, at JPL immense uncertainty is the perpetual driver for emerging technologies. AJ Abdallat has served as chief executive officer of Beyond Limits since its founding in 2014 and previously spear-headed several other startups that have been spun out of work first done at JPL. He believes that this firm’s pedigree gives it a big head start in pushing the envelope on cognitive computing because “the issues and challenges we are tackling today, JPL has been tackling for decades.” And though there remains a lively debate about where cognitive computing really stands today in terms of its human-like reasoning capabilities, Abdallat said, “People tend to judge AI based on the last 2 or 3 decades—you really need to look at what has happened in the past 5 to 6 years.” Some of the most notable recent advancements include the AI sector proving that intelligent programs can best human reasoning when it comes to complex games such as Go and Texas Hold’em Poker. Cognitive programs also demonstrate growing promise as a diagnostic tool in the healthcare industry—a vertical that Beyond Limits is positioning itself to break into along with oil and gas.

Promoting Sketching in Introductory Geoscience Courses: CogSketch Geoscience Worksheets.

Research from cognitive science and geoscience education has shown that sketching can improve spatial thinking skills and facilitate solving spatially complex problems. Yet sketching is rarely implemented in introductory geosciences courses, due to time needed to grade sketches and lack of materials that incorporate cognitive science research. Here, we report a design-centered, collaborative effort, between geoscientists, cognitive scientists, and artificial intelligence (AI) researchers, to characterize spatial learning challenges in geoscience and to design sketch activities that use a sketch-understanding program, CogSketch. We developed 26 CogSketch worksheets that use cognitive science-based principles to scaffold problem solving of spatially complex geoscience problems and report observations of an implementation in an introductory geoscience course where students used CogSketch or human-graded paper worksheets. Overall, this research highlights the principles of interdisciplinary design between cognitive scientists, geoscientists, and AI researchers that can inform the collaborative design process for others aiming to develop effective educational materials.

Cognitive Artificial Intelligence Method for Interpreting Transformer Condition Based on Maintenance Data

Cognitive Artificial Intelligence Method for Interpreting Transformer Condition Based on Maintenance Data

Trust reality-mining: evidencing the role of friendship for trust diffusion

Value sensitive design is driven by the motivation of making social and moral values central to the development of ICT systems. Among the most challenging concerns when imparting shared values like accountability, transparency, liberty, fairness and trust into information technology are reliable and comprehensive formal and computational models of those values. This paper, educated by trust theories and models from cognitive science, social sciences and artificial intelligence, proposes a novel stochastic computational model of trust, encapsulating abstractions of human cognitive capabilities and empirically evidenced social interaction patterns. Qualitative and quantitative features of trust are identified, upon which our formal model is phrased. Reality mining methods are used to validate the model based on a real life community dataset. We analyze the time-varying dynamics of the interaction and communication patterns of the community, consider varying types of relationships as well as their symmetry. Social network data analysis shows that our model better fits the evolved friendships compared to a well designed synthetic trust model, which is used as the baseline.

Woodside Energy Ltd: pioneer in cognitive computing, artificial intelligence and robotics

Cognitive computing is a new disruptive technology with the potential to reshape the oil and gas industry across the entire value chain. For Woodside Energy Ltd (Woodside), embracing this technology is an opportunity to save time, drive efficiency and reduce costs. In 2015, Woodside collaborated with IBM and deployed a cognitive computing system (IBM’s Watson) into its business. The system focuses on capturing the vast proprietary database of knowledge on Woodside’s major capital projects. Today, the Watson proof-of-concept has been successfully deployed by the science function into the business and is now under the care of the projects function. Moreover, it is undergoing continuous advances through further machine learning, additional ingestion of documentation and features linking the cognitive computer system with existing subject matter experts. Given the success of the first pilot program, Woodside is continuing to rapidly leverage cognitive technologies in other areas of the business. In mid-2016, Woodside deployed Watson for drilling events using IBM’s Watson Explorer – Advanced Addition. This program identifies and classifies a wide variety of geological drilling events allowing Woodside’s geoscience team to provide more timely and accurate assessment of potential risks for well design. Woodside continues to develop several other business solutions using these platforms in areas as diverse as continuous improvement, business management, maintenance campaigns, legal advice, general management and robotics. This presentation shares Woodside’s lessons and insights derived from its journey across multiple forms of cognitive technology and provides insights as to the state-of-the-art and adaptation of these systems to achieve specific goals.

The Five Pillars of a Cognitive Risk Framework—Part II

The purpose of this study is to develop the first cognitive risk framework for cybersecurity to address two narrative arcs in cyber warfare: the rise of the “hacker” as an industry and the “cybersecurity paradox”, namely why billions spent on cybersecurity fail to address semantic cyberattacks. Semantic cyberattacks, also known as social engineering, manipulates human users’ perceptions and interpretation of computer-generated data to obtain non-public confidential data. The cyber battleground has shifted from an attack on hard assets to a much softer target: the human mind. If human behavior is the new and last “weakest link” in the cybersecurity armor, is it possible to build cognitive defenses at the intersection of human-machine interactions? The answer is yes, but the change that is needed requires a new way of thinking about security, data governance and strategy. The concepts referenced in the Cognitive Risk Framework for Cybersecurity (CRFC) are drawn from a large body of research in multidisciplinary topics. Cognitive risk management is a sister discipline of a parallel body of science called Cognitive Informatics Security or CogSec. It is also important to point out as the creator of the CRFC, the principles and practices prescribed herein are borrowed from cognitive informatics security, machine learning, artificial intelligence (AI), and behavioral and cognitive science, among just a few that are still evolving. The Cognitive Risk Framework for Cybersecurity revolves around five pillars: Intentional Controls Design, Cognitive Informatics Security, Cognitive Risk Governance, Cybersecurity Intelligence and Active Defense Strategies and Legal “Best Efforts” considerations in Cyberspace. Complete text of “Cognitive Hack: The New Battleground in Cybersecurity … the Human Mind” is available here: https://www.crcpress.com/Cognitive-Hack-The-New-Battleground-in-Cybersecurity–the-Human-Mind/Bone/p/book/9781498749817

Engineering Modelling of Data Acquisition and Digital Instrumentation for Intelligent Learning and Recognition

In data acquisition and digital instrumentation fields, it is essential to understand the learning and recognition to acquire data and information of objects to be studied. In recent years, engineering modelling and simulation contribute greatly to the understanding of intelligent learning and recognition problems. The ability to learn is one of the central features of intelligence, which makes it an important concern for both cognitive psychology and artificial intelligence. In this paper, definitions and modelling aspects of learning are discussed. Fundamentals of learning and recognition and their applications are investigated and described. Illustrations are given to demonstrate the increasing applications of learning and recognition with engineering modelling in data acquisition and digital instrumentation fields.

Special issue on “Bounded Rationality updated”

From April 8th to 10th 2013, the Herbert Simon Society held its first General Conference in New York. About fifty researchers from different countries and working in different areas attended the event. The conference focused on three topics which were identified as particularly relevant in the development of Simonian thought: duality of mind, creativity and alternative theories to rational expectations. A first Herbert Simon Honorary Lecture by Gerd Gigerenzer opened the conference. Gerg Gigerenzer was later elected as Chairman of the Herbert Simon Society. Joseph Stiglitz closed the conference with the second Herbert Simon Honorary Lecture. The Herbert A. Simon Society brings together economists, social and cognitive scientists engaged in critical issues such as bounded rationality, problem solving, simulation of human thought and creativity. In particular, it gathers some of the most important economists who try to reformulate economic theory by starting from some of the non-neoclassical micro-foundations that have been developed in recent years. The Simon Society shares many interests with other Associations working on Behavioural Economics, Economic and Cognitive Social Psychology and Artificial Intelligence. This special issue collects some of the most interesting papers presented at the conference. Katherine Simon Frank, Herbert Simon’s daughter, made some welcoming remarks and talked about her father. In describing his way of being a

Cognitive Process of Moral Decision-Making for Autonomous Agents

There are a great variety of theoretical models of cognition whose main purpose is to explain the inner workings of the human brain. Researchers from areas such as neuroscience, psychology, and physiology have proposed these models. Nevertheless, most of these models are based on empirical studies and on experiments with humans, primates, and rodents. In fields such as cognitive informatics and artificial intelligence, these cognitive models may be translated into computational implementations and incorporated into the architectures of intelligent autonomous agents (AAs). Thus, the main assumption in this work is that knowledge in those fields can be used as a design approach contributing to the development of intelligent systems capable of displaying very believable and human-like behaviors. Decision-Making (DM) is one of the most investigated and computationally implemented functions. The literature reports several computational models that enable AAs to make decisions that help achieve their personal goals and needs. However, most models disregard crucial aspects of human decision-making such as other agents' needs, ethical values, and social norms. In this paper, the authors present a set of criteria and mechanisms proposed to develop a biologically inspired computational model of Moral Decision-Making (MDM). To achieve a process of moral decision-making believable, the authors propose a cognitive function to determine the importance of each criterion based on the mood and emotional state of AAs, the main objective the model is to enable AAs to make decisions based on ethical and moral judgment.

Children of the Fourth Revolution

It is a well-known fact that artificial intelligence (AI) research seeks both to reproduce the outcome of our intelligent behaviour by non-biological means, and to produce the non-biological equivalent of our intelligence. As a branch of engineering interested in intelligent behaviour reproduction, AI has been astoundingly successful. We increasingly rely on AI-related applications (smart technologies) to perform tasks that would be simply impossible by un-aided or un-augmented human intelligence. But as a branch of cognitive science interested in intelligence production, AI has been a dismal disappointment. Productive AI does not merely underperform with respect to human intelligence; it has not joined the competition yet. The fact that Watson—IBM’s system capable of answering questions asked in natural language—recently won against its human opponents when playing Jeopardy! only shows that artefacts can be smart without being intelligent. The two souls of AI, the engineering and the cognitive one, have often engaged in fratricidal feuds for intellectual predominance, academic power, and financial resources. That is partly because they both claim common ancestors and a single intellectual inheritance: a founding event, the Dartmouth Summer Research Conference on Artificial Intelligence in 1956, and a founding father, Turing, with his machine and its computational limits, and then his famous test. It hardly helps that a simulation might be used in order to check both whether the simulated source has been produced, and whether the targeted source’s behaviour or performance has been reproduced or even surpassed. The misalignment of their goals and results has caused endless and mostly pointless diatribes. Defenders of AI point to the strong results of reproductive, engineering AI, whereas detractors of AI point to the weak results of productive, cognitive AI. Many of the current speculations on the so-called singularity issue have their roots in such confusion. Philos. Technol. (2011) 24:227–232 DOI 10.1007/s13347-011-0042-7

Deleuze e Guattari e a Psicologia Cognitiva, IA e IHC: investigando possíveis conexões e diferenças.

The relation of Deleuze and Guattari’s works to cognitive science, Artificial Intelligence (AI), and Human Computer Interaction (HCI) is complicated: in some aspects, such as the ones touched upon in this article, the ontological commitments of classic cognitivism and traditional AI couldn’t be more distant from Deleuze and Guattari’s commitments to an expressionist philosophy. On the other hand, the ontological commitments of Deleuze and Guattari’s concept of the brain (Deleuze and Guattari, 1994), understood as a physiological organ that is shaped by external stimuli, is in some ways very close to those of cognitive science and more contemporarily, “brain science.” The purpose of this article is to enter into this dialogue between Deleuze and Guattari’s works and cognitive science and AI through three, related, Deleuzian concepts: desire’s relationship to objects (via part-objects), their concept of “maps” in relation to representation, and their concept of “machines” and “machinic” assemblages.

A Critique of the Theoretical and Empirical Literature of the Use of Diagrams, Graphs, and Other Visual Aids in the Learning of Scientific-Technical Content from Expository Texts and Instruction

This article presents a critical review and analysis of key studies that have been done in science education and other areas on the effects and effectiveness of using diagrams, graphs, photographs, illustrations, and concept maps as adjunct visual aids in the learning of scientific-technical content. It also summarizes and reviews those studies that have students draw diagrams, graphs, maps, and charts to express their understandings of the concepts and relationships that are present in the text they read or/and empirical data provided (i.e., student-generated adjunct visual productions). In general, the research and theory on instructional aids is fragmented and somewhat unsystematic with several flaws and a number of key uncontrolled variables, which actually suppress and mask effects in the studies that have been done. The findings of these studies are compared to relevant literature and empirical research and findings in the areas of cognitive psychology, computer science, neuroscience, and artificial intelligence that help to clarify many of the inconsistencies, contradictions, and lack of effects found for visual (e.g., diagrams and graphs) instructional aids in the science education literature currently and in the past 20 years. A model and a set of criteria and goals for improving research in this area is then described, as visuals are a first step in the process of learning formal (scientific) models, which are most often visually represented. Understanding how students learn formal models is one the outstanding research challenges in the next 20 years, both within and outside of science education.

A New Electronic Journal: NeuroQuantology or Two Sides of the Same Coin

NeuroQuantology (ISSN 1303-5150) is a refereed electronic journal dedicated to supporting the interdisciplinary exploration of the nature of quantum physics and its relation to the nervous system. NeuroQuantology publishes material relevant to that exploration from the perspectives afforded by the disciplines of cognitive science, philosophy, psychology, quantum physics, neuroscience and artificial intelligence. Interdisciplinary discussions are particularly encouraged.

Artificial Intelligence & Games: Should Computational Psychology be Revalued?

The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked.

Wonderful, but Not Incomprehensible: Multidisciplinary Perspectives on Herbert A. Simon and His Impact

When Nobel laureate Herbert A. Simon died on February 9, 2001, the fields of psychology, computer science, political science, public and business administration, economics, and sociology lost an intellectual giant recognized and honored as a leader, champion, scientific revolutionary, and builder. Beyond producing a prodigious body of seminal work in each of these areas, Simon was a founding father of cognitive psychology and artificial intelligence and the architect of multiple outstanding departments and programs at Carnegie Mellon University (CMU), his home base from 1947 to his death. Over a career that spanned more than six decades, Simon steadily raised the scientific rigor applied to explaining the behavior of economic, cognitive, administrative, political, computer, and social systems.

Evolving cognitive scaffolding and environment adaptation: a new research direction for evolutionary robotics

Many researchers in embodied cognitive science and artificial intelligence, and evolutionary robotics in particular, emphasize the interaction of brain, body and environment as crucial to the emergence of intelligent, adaptive behaviour. Accordingly, the interaction between agent and environment, as well as the co-adaptation of artificial brains and bodies, has been the focus of much research in evolutionary robotics. Hence, there are plenty of studies of robotic agents/species adapting to a given environment. Many animals, on the other hand, in particular humans, to some extent can choose to adapt the environment to their own needs instead of adapting (only) themselves. That alternative has been studied relatively little in robot experiments. This paper, therefore, presents some simple initial simulation experiments, in a delayed response task setting, that illustrate how the evolution of environment adaptation can serve to provide cognitive scaffolding that reduces the requirements for individual agents. Furthermore, theoretical implications, open questions and future research directions for evolutionary robotics are discussed.

The s-ethos system: A methodology for systematic flight analysis centered on human factors

The aeronautics community needs several alternative methods and tools to describe and analyze interactions between human operators and systems, according to some constraints (e.g., human factors, air safety, etc.). Hence, it needs to build models from the observation of real interactions, especially piloting, and to use extant theories from several fields: cognitive ergonomics and artificial intelligence, mainly. S-ETHOS sketches out a knowledge-based system that analyzes human pilot activities and provides feedback to improve air safety by giving measured appraisal of pilot error. The core of S-ETHOS is the ETHOS model that depicts the standard behavior based on the human pilot. S-ETHOS helps any air safety expert to simulate the pilot behavior during his mission and then will compare behavior between the simulation and real situations. It allows the air safety expert to know how the pilot assesses each situation. We implemented the ETHOS model according to an object-oriented approach, relying on a knowledge modeling language called OBJLOG II+. This model provides a first keystone to understanding how the human pilot captures and builds his environment through complex states. We will discuss the identified behaviors and potential deviations and associated situations.