Case-based reasoning (CBR) is widely applicable to the diagnosis of problems and the identification of solutions to them. This review of the literature identifies key papers relating to this use of CBR. The stages in diagnosing and troubleshooting a problem are considered and the elements of a CBR system for achieving this are described. Five systems are discussed that have frequently been cited in the literature and illustrate the use of CBR in diagnosis and troubleshooting applications.

Nakamoto proposed a new solution to transact value via the internet. And since 2009, blockchain technology has expanded and diversified. It has, however, proven to be inefficient in the way it achieves its outcomes, especially through the proof of work protocol. Other developers are promoting alternative methods but, as yet, none has superseded proof of work. The competing protocols illuminate a key feature of the blockchain community, namely, its inability to create consensus in a decentralized community. Because of this lack of consensus, the formation of standards is particularly difficult to achieve. At best standards are contested sites, and we examine three such sites where some form of agreement over standards will be essential if blockchain is to evolve successfully. These three sites are blockchain governance, smart contracts, and interoperability of blockchains. We argue that because standards’ formation is a contested and contingent process, the blockchain community will persist in creating difficulties and barriers for itself until it is able to resolve internal conflicts.

Agile software development (ASD) has emerged as a result of consolidated values proposed under the lightweight methods of software engineering. Despite bearing some criticisms, the initial deployment and results observed in the practice environment represents its increasing domination over the traditional software development practices. Any ASD method, in particular, requires knowledge-intensive practices and typically employs multi-disciplinary expert team working extended periods of time for weeks on a nearly continuous basis. A huge amount of tacit knowledge creation and exchange happens in the entire process over the project lifecycle, which attracts the attention of research in the domain of knowledge management (KM). In this paper, first, we have mapped the agile values and agile principles, and in its support, we have argued upon and the need for integrated KM infrastructure and proposed a KM model that can be employed within the organization. We have also developed a conceptual framework for knowledge sharing and learning for the individual practitioners for the sustainability of agile team. We attempt to create an organizational learning framework for knowledge creation and exchange among the involved entities in a collaborative practice environment.

This paper presents a methodology that permits to automate binary classification using the minimum possible number of attributes. In this methodology, the success of the binary prediction does not lie in the accuracy of an algorithm but in the evaluation metrics, which give information about the goodness of fit; which is an important factor when the data batch is unbalanced. The proposed methodology assesses the possible biases in identifying one algorithm as the best performer when considering the goodness of fit of an algorithm through evaluation metrics. The dimension of data has been reduced through the cumulative explained variance. Then, the performance of six machine learning classification models has been compared through Matthew correlation coefficient (MCC), area under curve – receiver operating characteristic (ROC-AUC), and area under curve – precision-recall (AUC-PR). The results show graphically and numerically how the evaluation metrics interfere with the most optimal outcome of an algorithm. The algorithms with the best performance in terms of evaluation metrics have been random forest and gradient boosting. In the imbalanced datasets, MCC has provided better prediction results than ROC-AUC or AUC-PR. The proposed methodology is adapted to the case of bankruptcy prediction.

In this paper we describe the framework we have developed in KIDS (Kestrel Interactive Development System) for generating efficient constraint satisfaction programs. We have used KIDS to synthesise global search scheduling programs that have proved to be dramatically faster than other programs running the same data. We focus on the underlying ideas that lead to this efficiency. The key to the efficiency is the reduction of the size of the search space by an effective representation of sets of possible solutions (solution spaces) that allows efficient constraint propagation and pruning at the level of solution spaces. Moving to a solution space representation involves a problem reformulation. Having found a solution to the reformulated problem, an extraction phase extracts solutions to the original problem. We show how constraints from the original problem can be automatically reformulated and specialised in order to derive efficient propagation code automatically. Our solution methods exploit the semi-lattice structure of our solution spaces.

Coordinating the actions of agents in multiagent systems presents a challenging problem, especially as the size of the system is increased and predicting the agent interactions becomes difficult. Many approaches to improving coordination within multiagent systems have been developed including organizational structures, shaped rewards, coordination graphs, heuristic methods, and learning automata. However, each of these approaches still have inherent limitations with respect to coordination and scalability. We explore the potential of synergistically combining existing coordination mechanisms such that they offset each others’ limitations. More specifically, we are interested in combining existing coordination mechanisms in order to achieve improved performance, increased scalability, and reduced coordination complexity in large multiagent systems.

In this work, we discuss and demonstrate the individual limitations of two well-known coordination mechanisms. We then provide a methodology for combining the two coordination mechanisms to offset their limitations and improve performance over either method individually. In particular, we combine shaped difference rewards and hierarchical organization in the Defect Combination Problem with up to 10 000 sensing agents. We show that combining hierarchical organization with difference rewards can improve both coordination and scalability by decreasing information overhead, structuring agent-to-agent connectivity and control flow, and improving the individual decision-making capabilities of agents. We show that by combining hierarchies and difference rewards, the information overheads and computational requirements of individual agents can be reduced by as much as 99% while simultaneously increasing the overall system performance. Additionally, we demonstrate the robustness of this approach to handling up to 25% agent failures under various conditions.

Approximate consensus is an important building block for distributed systems, used overtly or implicitly in applications as diverse as formation control, sensor fusion, and synchronization. Laplacian-based consensus, the current dominant approach, is extremely accurate and resilient, but converges slowly. Comparing Laplacian-based consensus to exact consensus algorithms, relaxing the requirements for accuracy and resilience should enable a spectrum of algorithms that incrementally tradeoff accuracy and/or resilience for speed. This manuscript demonstrates that may be so by beginning to populate this spectrum with a new approach to approximate consensus, Power-Law-Driven Consensus (PLD-consensus), which accelerates consensus by sending values across long distances using a self-organizing overlay network. Both a unidirectional and bidirectional algorithm based on this approach are studied. Although both have the same asymptotic O(diameter) convergence time (vs. O(diameter 2) for Laplacian-based), unidirectional PLD-consensus is faster and more resilient than bidirectional PLD-consensus, but exhibits higher variance in the converged value.

Modelling is one of the most frequent tasks in the area of information systems (ISs), with models such as schemata, ontologies, patterns and architectures forming the bases for their creation. Very often, however, the difference between these types of models is not clear and causes confusion and erratic use of the terms. The aim of this paper is to clarify the concept of model through a study of some of the more common ones used in ISs. This proposal is presented through an ontology, where we show how we conceptualize models according to their role in the development of an IS: the model understood as the representation of a domain or as a reality serving as an example.

This paper provides a survey of the state of the art in plausible reasoning, that is exception tolerant reasoning under incomplete information. Three requirements are necessary for a formalism in order to cope with this problem: (i) making a clear distinction between factual information and generic knowledge; (ii) having a correct representation of partial ignorance; (iii) providing a nonmonotonic inference mechanism. Classical logic fails on requirements (i) and (iii), whilst the Bayesian approach does not fulfil (ii) in an unbiased way. In this perspective, various uncertainty modelling frameworks are reviewed: MYCIN-like fully compositional calculi, belief functions, upper and lower probability systems, and possibility theory. Possibility theory enables classical logic to be extended to layered sets of formulae, where layers express certainty levels. Finally, it is explained how generic knowledge can be expressed by constraints on possibility measures, and how possibilistic inferences can encode nonmonotonic reasoning in agreement with the Lehmann et al. postulates.

Research into visual reasoning up to now has focused on images that are literal depictions of their objects. I argue in this article that an important further mode of visual reasoning operates on images that depict objects metaphorically. Such images form part of the class of expressive symbols: they are found, for example, in allegorical representations in works of visual art, studied by iconology. They were also a common way of encapsulating insights about the universe in natural philosophy in the Renaissance. Many writers assume that expressive symbols have vanished from modern science, but I argue in the second part of the article that mathematical law statements in present-day physics should be seen, in part, as images that constitute expressive symbols of the world. In support of this view, I offer evidence that law statements relate to their objects metaphorically and that physicists engage with them primarily through visual inspection and visual reasoning.

Knowledge engineering (KE) has now provided some effective techniques for formalization of knowledge about goals and actions. These techniques could open new areas of research to statisticians. Experimental systems designed to assist users of statistics have been constructed in experiment design, data analysis, technique application, and technique selection. Knowledge formalization has also been used in experimental programs to assist statisticians in doing data analysis and in building consultation systems. The best-explored application of KE techniques is building consultation systems. It is now a promising area for development. Analogies with successful artificial intelligence AI applications in other fields suggest other statistical applications worth exploring. Opening new areas to research and providing new tools to users would make considerable changes in the use and production of statistical techniques. However, applying currently available KE techniques will lead to more work for statisticians, not less.

We consider task planning for long-living intelligent agents situated in dynamic environments. Specifically, we address the problem of incomplete knowledge of the world due to the addition of new objects with unknown action models. We propose a multilayered agent architecture that uses meta-reasoning to control hierarchical task planning and situated learning, monitor expectations generated by a plan against world observations, forms goals and rewards for the situated reinforcement learner, and learns the missing planning knowledge relevant to the new objects. We use occupancy grids as a low-level representation for the high-level expectations to capture changes in the physical world due to the additional objects, and provide a similarity method for detecting discrepancies between the expectations and the observations at run-time; the meta-reasoner uses these discrepancies to formulate goals and rewards for the learner, and the learned policies are added to the hierarchical task network plan library for future re-use. We describe our experiments in the Minecraft and Gazebo microworlds to demonstrate the efficacy of the architecture and the technique for learning. We test our approach against an ablated reinforcement learning (RL) version, and our results indicate this form of expectation enhances the learning curve for RL while being more generic than propositional representations.

This paper presents our preliminary research into the autonomous control of an alpine skiing robot. Based on our previous experience with active balancing on difficult terrain and developing an ice-skating robot, we have implemented a simple control system that allows the humanoid robot Jennifer to steer around a simple alpine skiing course, brake, and actively control the pitch and roll of the skis in order to maintain stability on hills with variable inclination.

The robot steers and brakes by using the edges of the skis to dig into the snow, by inclining both skis to one side the robot can turn in an arc. By rolling the skis outward and pointing the toes together the robot creates a snowplough shape that rapidly reduces its forward velocity.

To keep the skis in constant contact with the hill we use two independent proportional-integral-derivative (PID) controllers to continually adjust the robot’s inclination in the frontal and sagittal planes.

Our experiments show that these techniques are sufficient to allow a small humanoid robot to alpine ski autonomously down hills of different inclination with variable snow conditions.

The terminology of Machine Learning and Data Mining methods does not always allow a simple match between practical problems and methods. While some problems look similar from the user's point of view, but require different methods to be solved, some others look very different, yet they can be solved by applying the same methods and tools. Choosing appropriate Machine Learning methods for problem solving in practice is therefore largely a matter of experience and it is not realistic to expect a simple look-up table with matches between problems and methods. However, some guidelines can be given and a collection that summarizes other people's experience can also be helpful. A small number of definitions characterize the tasks that are performed by a large proportion of methods. Most of the variation in methods is concerned with differences in data types and algorithmic aspects of methods. In this paper, we summarize the main task types and illustrate how a wide variety of practical problems are formulated in terms of these tasks. The match between problems and tasks is illustrated with a collection of example applications with the aim of helping to express new practical problems as Machine Learning tasks. Some tasks can be decomposed into subtasks, allowing a wider variety of matches between practical problems and (combinations of) methods. We review the main principles for choosing between alternatives and illustrate this with a large collection of applications. We believe that this provides some guidelines.

The traditions of image processing and knowledge engineering have developed separately. Work on AI vision systems lies between the two traditions but only recently has attention been given to combining practical imaging systems with methods for exploiting knowledge in interpreting the contents of an image. Five general approaches to combining knowledge based expert systems with imaging technologies are discussed. Particular attention is paid to the requirement for techniques which transform a pixel array into a symbolic form suitable for interpretation, and current obstacles to a general solution. Interpretation of biomedical images is particularly problematic because of statistical, structural and temporal variation in morphology of objects and structures. Some ways in which knowledge of shape, structure, and object classifications may contribute to this interpretation are discussed. The survey focuses on biomedical images but many of the issues are of general relevance to work in image understanding.

Academic work on agents and ontologies is often oblivious to the complexities and realities of enterprise computing. At the same time, the practitioners of enterprise computing, although they are adept at the building of robust, real-life enterprise applications, are unaware of the academic body of work and the opportunities of applying novel approaches of academic origin. Enterprise applications are very complex systems that are designed to support critical business operations. This article outlines the technical and business foundations of enterprise application software and briefly discusses viable opportunities for agents and ontology research.

In the coming years, the Web is expected to evolve from a structure containing information resources that have little or no explicit semantics to a structure having a rich semantic infrastructure. The key defining feature that is intended to distinguish the future Semantic Web from today's Web is that the content of the Web will be usable by machines (i.e. software agents). Meaning needs to be communicated between agents who advertise and/or require the ability to perform tasks on the Web. Agents also need to determine the meaning of passive (i.e. non-agent) information resources on the web to perform these tasks.