Cognitive models of causal inferences using causation networks

Autonomous on-line knowledge discovery and acquisition play an important role in cognitive informatics, cognitive computing, knowledge engineering, and computational intelligence. On the basis of the latest advances in cognitive informatics and denotational mathematics, this paper develops a web knowledge discovery engine for web document restructuring and comprehension, which decodes on-line knowledge represented in informal documents into cognitive knowledge represented by concept algebra and concept networks. A visualized concept network explorer and a semantic analyzer are implemented to capture and refine queries based on concept algebra. A graphical interface is built using concept and semantic models to refine users’ queries. To enable autonomous information restructuring by machines, a two-level knowledge base that mimics human lexical/syntactical and semantic cognition is introduced. The information restructuring model provides a foundation for automatic concept indexing and knowledge extraction from web documents. The web knowledge discovery engine extends machine learning capability from imperative and adaptive information processing to autonomous and cognitive knowledge processing with unstructured documents in natural languages.

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

Cognitive linguistics is an emerging discipline that studies the cognitive properties of natural languages and the cognitive models of languages in computational linguistics, cognitive computing, and computational intelligence. This paper presents the theoretical framework of cognitive linguistics in order to systematically formalize the syntaxes and grammars of natural languages. An abstract language model of cognitive linguistics is created at the top level. Based on it, the cognitive structures of languages at the levels of lexis, phrase, clauses, sentence, paragraph, and essay are formally modeled from the bottom up. Using contemporary denotational mathematics, the deductive grammar of English is formally modeled and rigorously analyzed. The basic research provides support for a wide range of applications in computational linguistics, cognitive informatics, online text processing, web search engines, machine language comprehension, autonomous machine learning, smart cell phones, semantic computing, and computing with words.

The Seventh International Symposium on Neural Networks (ISNN 2010) was held on June 6–9, 2010 in Shanghai, China. The ISNN 2010 was a great success and provided a high-level international forum for scientists and engineers to present the latest research in neural networks, computational intelligence, cognitive systems, and related fields. To highlight the success of this conference, we edited this special issue for Cognitive Computation. We chose nine papers from over 591 papers submitted to ISNN 2010, which reflect the high quality of the presentations at the conference while capturing the spirit of our theme, ‘‘Advances in Computational Intelligence and Applications,’’ for this special issue. Our goal for this special issue is to present the latest research developments in computational intelligence with a focus on cognitive computation and their applications across a wide range of domains. Over the past decades, we have witnessed tremendous interest and developments in all aspects of computational intelligence research, ranging from both theoretical foundations, principles, to practical applications in different domains. To reflect a flavor of recent research activities in the community, we carefully selected these nine papers for this special issue. The selected papers can be organized into the following four coherent sections. The first section is directly related to neural network models and applications. The paper by Deng presents a new control strategy for the chaotic neural network, in which the refractoriness is tuned by using feedback control based on online averaging of network states. Simulation results demonstrate that the proposed model can achieve favorable performance in handling noisy, incomplete, and composite patterns, while also achieving either enhanced or comparable memory capacity compared with the classic Hopfield net and other chaotic neural network models. In the other paper, Gluge et al. discuss recurrent networks for implicit learning of temporal sequences, a very important area to understand high-level cognitive intelligence. The authors propose a recurrent network with backpropagation training algorithm that is adapted to the reinforcement learning scheme. The simulation results fit quantitatively as well as qualitatively to the behavioral results, suggesting the role of temporal context in associative learning scenarios. The second section is related to language processing and understanding, an important topic in cognitive science and computational intelligence. The paper by Murata et al. investigates the maximum entropy (ME) approach for natural language processing (NLP) problems including machine translation and information extraction. Comparative studies of the proposed approach with the existing techniques are illustrated in this paper. In related work to this field, the paper by Kaznatcheev investigates connectionist models on the interplay of nouns and pronouns in personal pronoun acquisition, a very interesting and important problem in psychology. For instance, the author discusses the learning in the shifting reference situation for children. It is reported that learning of two different noun-and-pronoun addressee patterns is consistent with Z. Zeng (&) Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China e-mail: zgzeng@gmail.com

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing.

Cognitive Informatics is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, software engineering, AI, cybernetics, cognitive science, neuropsychology, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. This editorial introduces the emerging field of cognitive informatics and its applications in cognitive computing, abstract intelligence, computational mathematics, and computational intelligence. The themes and structure of this special issue on cognitive informatics are described, and then, focuses of the selected papers in Part II of this special issue are highlighted.

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

The contemporary wonder of sciences and engineering has recently refocused on the starting point of them: How the brain processes internal and external information autonomously and cognitively rather than imperatively as those of conventional computers do? This leads to the advances in the field of Cognitive Informatics (CI) as a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science. CI investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC'11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world's renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

Autonomous Systems (AS) are perceived as the most advanced intelligent systems evolved from those of reflexive, imperative, and adaptive intelligence. A plenary panel on 'Future Development of Autonomous Systems' is organized at the inaugural IEEE ICAS'21. This paper reports the panel discussions about the-state-of-the-art and paradigms of AS, the basic research on theoretical foundations and mathematical means of AS, and challenges to the future development of AS. As an emerging and increasingly demanded field, AS provide an unprecedented approach to contemporary intelligent industries including deep machine learning, highly intelligent robotics, cognitive computers, general AI technologies, and industrial applications enabled by transdisciplinary advances in intelligence science, system science, brain science, cognitive science, robotics, computational intelligence, and intelligent mathematics.

Cognitive computing is the development of computerized models to mimic human behavior. The best examples are virtual assistants such as Siri, Alexa, Cortana, etc. Cognitive computing and AI play a big role in solving problems and building applications to support several domains in Internet of Things (IoT). The downside to AI and cognitive computing is the complexity of the architecture involved in building models that supports IoT. Toward the late 1960s, researchers came up with a technology called cloud computing that allowed individuals and organizations to make use of services provided by the cloud to solve problems. This meant that one no longer needed to worry about the software and hardware components required to develop intelligent models. Although cloud computing has been able to solve most complexity issues, it has several drawbacks of its own – latency, privacy, security, reliability, etc. Domains like healthcare rely on intelligent models to make important time-sensitive decisions and cannot wait for data to be shipped all the way to the cloud to get results. The need for quick and real-time results is what probed researchers to develop Edge Intelligence or Cognitive Computing at the Edge with IoT application services. Edge Intelligence is the deployment of intelligent computerized models close to the data source – on the edge node of an IoT network or very near to the network edge. This chapter highlights and examines the principles, architecture, challenges, applications, and existing industrial implementations of Edge Intelligence.

The paper considers the issues of implementation and application of intelligent computing on the basis of cognitive and event modeling in research on energy security. The authors suggest a two-level information technology for the research. The first level suggests a situation analysis using the intelligent computing techniques. The analysis results are then used to choose rational variants of energy development in Russia (or its regions). At the second level these variants are computed with the multi-agent software INTEC-M. Transition from the first to the second level is automated by the tools of deductive program synthesis, that are based on declarative descriptions, i.e. formulae of restricted predicate calculus, and representation of input data by XML files. Cognitive and event modeling is considered in more detail. The examples of cognitive and event models are presented. The structure of a knowledge space is developed to support the intelligent computations. The knowledge space includes ontological models, databases of cognitive and event models, and the database on the cases of energy emergency situations. The authors developed the CogMap and EventMap tools to support cognitive and event modeling on the basis of common graphical environment GirModeling, and the expert system “Emergency”. The tools and expert system that support the knowledge base on energy emergencies are integrated within the intelligent IT environment. The research presented in the paper was partially supported by the grant of Presidium of RAS No. 2.2-2012 and grants of Russian Foundation of Basic Research No. 10-07-00264, No. 11-07-00192, No. 11-07-00245, and No. 12-07-00359.

Psychologists have long had interest in the processes through which antecedent variables produce their effects on the outcomes of ultimate interest (e.g., Wood-worth's Stimulus-Organism-Response model). Models involving such meditational processes have characterized many of the important psychological theories of the 20th century and continue to the present day. However, it was not until Judd and Kenny (1981) and Baron and Kenny (1986) combined ideas from experimental design and structural equation modeling that statistical methods for directly testing such models, now known as mediation analysis, began to be developed. Methodologists have improved these statistical methods, developing new, more efficient estimators for mediated effects. They have also extended mediation analysis to multilevel data structures, models involving multiple mediators, models in which interactions occur, and an array of noncontinuous outcome measures (see MacKinnon, 2008). This work nicely maps on to key questions of applied researchers and has led to an outpouring of research testing meditational models (As of August, 2011, Baron and Kenny's article has had over 24,000 citations according to Google Scholar).

The papers in this special section focus on emerging computational intelligence (CI) based theories and methodologies for big graph data management. The emerging CI techniques cover a broad range of nature-inspired, multidisciplinary computational methodologies, such as fuzzy logic, graph pattern matching, graph neural networks, graph embedding, graph attention, evolutionary computing, cognitive computing, learning theory, and probabilistic methods. The objective of this special issue is to explore how CI models and their variants can be adapted, augmented and extended to deal with applications involving very large scale graph data.

Justifying operational decisions for robots is a challenging task as the operator or the robot itself has to understand the underlying physical interaction between the robot and the environment to predict the potential outcome. It is desirable to understand how the decision influences the operational performance in the way of causal relationship for the purpose of explainable decision-making. Here we propose a novel causal inference framework for the discovery and inference on the reasoning of the operational decisions for robots. It unifies both domain knowledge integration and model-free causal inference, allowing a data-driven causal knowledge learning on time series data. The framework is evaluated in the experiments of an underwater robot with complex environmental interactions. The results show that the framework can learn the causal structure and inference model to accurately explain and predict the operation performance with integrated physics.

Many artificial intelligence (AI) theoretical models are inspired by human/natural/social intelligence mechanisms. Three main schools of artificial intelligence have been formed, that is, symbolism, connectionism and behaviorism. Cognitive computing is one of the key fields of AI. It is a critical task for AI researchers to develop advanced cognitive computing models. Cognitive computing is the third and the most transformational phase in computing’s evolution, after the Tabulating Era and Programming Era. Inspired by human’s granularity thinking based problem solving mechanism and the cognition law of “global precedence”, a data-driven granular cognitive computing model (DGCC) is proposed in this paper. It integrates two contradictory mechanisms, namely, human’s cognition mechanism of “global precedence” which is a cognition process of “from coarser to finer” and the information processing mechanism of machine learning systems which is “from finer to coarser”. According to DGCC, deep learning is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper.