As one of fundamental properties to characterize inconsistency measures for knowledge bases, the property of free formula independence well captures the intuition that free formulas are independent of the amount of inconsistency in a knowledge base for cases where inconsistency is characterized in terms of minimal inconsistent subsets. But it has been argued that not all the free formulas are independent of inconsistency in some other contexts of inconsistency characterization. In this paper, we propose a characterization of formulas independent of inconsistency in the framework of Priest's minimally inconsistent LP. Based on an atom-based counterpart of the notion of free formula, we propose a notion of Bi-free formula to describe formulas that are free from inconsistency in both syntax and paraconsistent models in this logic. Then we propose the property of Bi-free formula independence, which is more suitable for characterizing the role of formulas free from inconsistency in measuring inconsistency from both syntactic and semantic perspectives.

The main purposes of this study are to distinguish the trends of research in publication exits for the utilisations of the fuzzy expert and knowledge-based systems that is done based on the classification of studies in the last decade. The present investigation covers 60 articles from related scholastic journals, International conference proceedings and some major literature review papers. Our outcomes reveal an upward trend in the up-to-date publications number, that is evidence of growing notoriety on the various applications of fuzzy expert systems. This raise in the reports is mainly in the medical neuro-fuzzy and fuzzy expert systems. Moreover, another most critical observation is that many modern industrial applications are extended, employing knowledge-based systems by extracting the experts' knowledge.

For the diagnostic inference under uncertainty Bayesian networks are investigated. The method is based on an adequate uniform representation of the necessary knowledge. This includes both generic and experience-based specific knowledge, which is stored in a knowledge base. For knowledge processing, a combination of the problem-solving methods of concept-based and case-based reasoning is used. Concept-based reasoning is used for the diagnosis, therapy and medication recommendation and evaluation of generic knowledge. Exceptions in the form of specific patient cases are processed by case-based reasoning. In addition, the use of Bayesian networks allows to deal with uncertainty, fuzziness and incompleteness. Thus, the valid general concepts can be issued according to their probability. To this end, various inference mechanisms are introduced and subsequently evaluated within the context of a developed prototype. Tests are employed to assess the classification of diagnoses by the network.

Domain-specific heuristics are an important technique for solving combinatorial problems efficiently. We propose a novel semantics for declarative specifications of domain-specific heuristics in Answer Set Programming (ASP). Decision procedures that are based on a partial solution are a frequent ingredient of existing domain-specific heuristics, e.g., for placing an item that has not been placed yet in bin packing. Therefore, in our novel semantics negation as failure and aggregates in heuristic conditions are evaluated on a partial solver state. State-of-the-art solvers do not allow such a declarative specification. Our implementation in the lazy-grounding ASP system Alpha supports heuristic directives under this semantics. By that, we also provide the first implementation for incorporating declaratively specified domain-specific heuristics in a lazy-grounding setting. Experiments confirm that the combination of ASP solving with lazy grounding and our novel heuristics can be a vital ingredient for solving industrial-size problems.

Anchorage may not be the most well-connected location in the world. But as it turns out, when people and data are well-connected, location may follow. Anchorage was host to SIGKDD's Conference on Knowledge Discovery and Data Mining in 2019 or KDD as it's commonly known. The conference is organized by the Association for Computing Machinery (ACM)'s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). KDD is one of the most well-known and popular events for data science and AI, attracting around 3.500 researchers in 2018 in London.

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise, and is further challenged by today's often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts, have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems.

Artificial intelligence (AI) and machine learning are crucial to modern-day business success, which is why nearly 60% of organizations have deployed AI, according to Gartner's recent Survey Analysis: AI and ML Development Strategies, Motivators and Adoption Challenges. AI projects are predicted to grow, resulting in an increase in AI staffing numbers in the next three years, KPMG's AI Transforming the Enterprise report added. However, the number of AI-specific employees aren't the only ones expected increase, PMI's Pulse of the Profession survey reported on Thursday. As the number of AI projects increase, so do the number of individuals managing those projects: Over the next three years, 27% of respondents said AI will lead to the creation of more project management jobs. "While there's a fear that AI will replace jobs, AI has – and will continue – to open up opportunities for project managers," said Mike DePrisco, PMI's vice president of global solutions.

In this paper, we introduce an extension of our presented cognitive-based emotion model [27][28]and [30], where we enhance our knowledge-based emotion unit of the architecture by embedding a fuzzy rule-based system to it. The model utilizes the cognitive parameters dependency and their corresponding weights to regulate the robot's behavior and fuse their behavior data to achieve the final decision in their interaction with the environment. Using this fuzzy system, our previous model can simulate linguistic parameters for better controlling and generating understandable and flexible behaviors in the robots. We implement our model on an assistive healthcare robot, named Robot Nurse Assistant (RNA) and test it with human subjects. Our model records all the emotion states and essential information based on its predefined rules and learning system. Our results show that our robot interacts with patients in a reasonable, faithful way in special conditions which are defined by rules. This work has the potential to provide better on-demand service for clinical experts to monitor the patients' emotion states and help them make better decisions accordingly.

As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.

Ask a successful startup seed investor, "How do you do it?" Press harder and you will likely hear, "It takes years of apprenticeship and experience in pattern recognition." The myth of expert magic was challenged decades ago but it still persists. If you wanted to know whether you had cancer or not, you were better off using the algorithm that the researchers had created than you were asking the radiologist to study the X-ray. The simple algorithm had outperformed not merely the group of doctors; it had outperformed even the single best doctor.