UnCommonSense: Informative Negative Knowledge about Everyday Concepts

Commonsense knowledge about everyday concepts is an important asset for AIapplications, such as question answering and chatbots. Recently, we have seenan increasing interest in the construction of structured commonsense knowledgebases (CSKBs). An important part of human commonsense is about properties thatdo not apply to concepts, yet existing CSKBs only store positive statements.Moreover, since CSKBs operate under the open-world assumption, absentstatements are considered to have unknown truth rather than being invalid. Thispaper presents the UNCOMMONSENSE framework for materializing informativenegative commonsense statements. Given a target concept, comparable conceptsare identified in the CSKB, for which a local closed-world assumption ispostulated. This way, positive statements about comparable concepts that areabsent for the target concept become seeds for negative statement candidates.The large set of candidates is then scrutinized, pruned and ranked byinformativeness. Intrinsic and extrinsic evaluations show that our methodsignificantly outperforms the state-of-the-art. A large dataset of informativenegations is released as a resource for future research.<br

UnCommonSense: Informative Negative Knowledge about Everyday Concepts

Commonsense knowledge about everyday concepts is an important asset for AIapplications, such as question answering and chatbots. Recently, we have seenan increasing interest in the construction of structured commonsense knowledgebases (CSKBs). An important part of human commonsense is about properties thatdo not apply to concepts, yet existing CSKBs only store positive statements.Moreover, since CSKBs operate under the open-world assumption, absentstatements are considered to have unknown truth rather than being invalid. Thispaper presents the UNCOMMONSENSE framework for materializing informativenegative commonsense statements. Given a target concept, comparable conceptsare identified in the CSKB, for which a local closed-world assumption ispostulated. This way, positive statements about comparable concepts that areabsent for the target concept become seeds for negative statement candidates.The large set of candidates is then scrutinized, pruned and ranked byinformativeness. Intrinsic and extrinsic evaluations show that our methodsignificantly outperforms the state-of-the-art. A large dataset of informativenegations is released as a resource for future research.<br

Evaluation of a fuzzy-expert system for fault diagnosis in power systems

A major problem with alarm processing and fault diagnosis in power systems is the reliance on the circuit alarm status. If there is too much information available and the time of arrival of the information is random due to weather conditions etc., the alarm activity is not easily interpreted by system operators. In respect of these problems, this thesis sets out the work that has been carried out to design and evaluate a diagnostic tool which assists power system operators during a heavy period of alarm activity in condition monitoring. The aim of employing this diagnostic tool is to monitor and raise uncertain alarm information for the system operators, which serves a proposed solution for restoring such faults. The diagnostic system uses elements of AI namely expert systems, and fuzzy logic that incorporate abductive reasoning. The objective of employing abductive reasoning is to optimise an interpretation of Supervisory Control and Data Acquisition (SCADA) based uncertain messages when the SCADA based messages are not satisfied with simple logic alone. The method consists of object-oriented programming, which demonstrates reusability, polymorphism, and readability. The principle behind employing objectoriented techniques is to provide better insights and solutions compared to conventional artificial intelligence (Al) programming languages. The characteristics of this work involve the development and evaluation of a fuzzy-expert system which tries to optimise the uncertainty in the 16-lines 12-bus sample power system. The performance of employing this diagnostic tool is assessed based on consistent data acquisition, readability, adaptability, and maintainability on a PC. This diagnostic tool enables operators to control and present more appropriate interpretations effectively rather than a mathematical based precise fault identification when the mathematical modelling fails and the period of alarm activity is high. This research contributes to the field of power system control, in particular Scottish Hydro-Electric PLC has shown interest and supplied all the necessary information and data. The AI based power system is presented as a sample application of Scottish Hydro-Electric and KEPCO (Korea Electric Power Corporation)

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book

Knowledge and Reasoning for Image Understanding

abstract: Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond factual recall of the recognized components and includes reasoning and thinking beyond what can be seen (or perceived). Understanding is often evaluated by asking questions of increasing difficulty. Thus, the expected functionalities of an intelligent Image Understanding system can be expressed in terms of the functionalities that are required to answer questions about an image. Answering questions about images require primarily three components: Image Understanding, question (natural language) understanding, and reasoning based on knowledge. Any question, asking beyond what can be directly seen, requires modeling of commonsense (or background/ontological/factual) knowledge and reasoning. Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Bayesian Abduction Model For Sensemaking

This research develops a Bayesian Abduction Model for Sensemaking Support (BAMSS) for information fusion in sensemaking tasks. Two methods are investigated. The first is the classical Bayesian information fusion with belief updating (using Bayesian clustering algorithm) and abductive inference. The second method uses a Genetic Algorithm (BAMSS-GA) to search for the k-best most probable explanation (MPE) in the network. Using various data from recent Iraq and Afghanistan conflicts, experimental simulations were conducted to compare the methods using posterior probability values which can be used to give insightful information for prospective sensemaking. The inference results demonstrate the utility of BAMSS as a computational model for sensemaking. The major results obtained are: (1) The inference results from BAMSS-GA gave average posterior probabilities that were 103 better than those produced by BAMSS; (2) BAMSS-GA gave more consistent posterior probabilities as measured by variances; and (3) BAMSS was able to give an MPE while BAMSS-GA was able to identify the optimal values for kMPEs. In the experiments, out of 20 MPEs generated by BAMSS, BAMSS-GA was able to identify 7 plausible network solutions resulting in less amount of information needed for sensemaking and reducing the inference search space by 7/20 (35%). The results reveal that GA can be used successfully in Bayesian information fusion as a search technique to identify those significant posterior probabilities useful for sensemaking. BAMSS-GA was also more robust in overcoming the problem of bounded search that is a constraint to Bayesian clustering and inference state space in BAMSS

Design as a functional leader: a case study to investigate the role of design as a potential leading discipline in multinational organisations

This research investigates the role of design as a ‘functional leader’1 in multinational organisations, to drive innovation successfully at a strategic level. It involved a detailed case study of the innovation process, and practices within Philips Design, Eindhoven, The Netherlands, where design is a key function within the company but not yet recognised as a leading strategic discipline. Philips Design wanted design research to build an integrated map of its actual practices and correlate these with other corporate innovation practices, to help establish strategic recognition for their value. The doctoral challenge was to explicate the process and determine whether the findings have generic capacity to support the role of design as a leading functional discipline. The investigation integrates an iterative loop of; abductive reasoning of design thinking and inductive reasoning of management thinking in an action research cycle. The case study was an empirical enquiry, where the researcher became a ‘participatory observer’ at Philips Design, conducting one-on-one interviews for data collection and refining their analysis using a Delphi Technique. The contribution to knowledge has been generated by combining these research methods to represent data in a logical manner using visual mapping techniques to produce an explicitly defined ‘design innovation process map’ for Philips Design. Comparison with three other multinational organisations explored how each perceives the contribution of design and the different roles it plays in their organisation. Triangulation with a third party expert was also used to validate the findings. The correlation of the research with literature in the field explored the relationship between human behaviour, organisational culture and business innovation cycles and took this an incremental step forward by visually illustrating the conceptual relationship between different theories. The focus became understanding the reasons for the differences between the thinkers and the practitioners in a design team. Significantly, this led to it validating the theory of ‘Design Driven Innovation’ by Roberto Verganti (2009). The study contributes value to his theory of innovation by highlighting four gaps in its application in multinational organisations and demonstrates that design can share the role of innovation leadership with other important functions only if it has an explicit process that aligns with organisational brand values and communicates the value generated by design effectively to the wider team. Therefore, whilst the research has not been able to confirm whether design can lead an effective innovation process at a strategic level, rather it needs to share this role in multinational organisations, it has identified the major reason for this as the differences between design team thinkers trying to find viable options for the future and practitioners trying to defend the core business in their organisation, resulting in a gap between strategy and operation. The research has confirmed the conditions for design to act as a leading functional discipline and provided design practitioners with tools that can help in strategic decision-making. It is hoped this research will inspire design researchers to carry out further study on the topic to improve and develop knowledge and competency to support the strategic role of design as a leading functional discipline in organisations. Also, that business, strategy and marketing researchers will be inspired to generate theories that could link the strategic role of the design innovation process to strategies in their own fields. Finally, the research identifies the need for quantitative research to explain the qualitative conceptual relationships it has depicted between designer behaviour and organisational culture in the different innovation cycles that exist in multinational organisations

Instructional strategies in explicating the discovery function of proof for lower secondary school students

In this paper, we report on the analysis of teaching episodes selected from our pedagogical and cognitive research on geometry teaching that illustrate how carefully-chosen instructional strategies can guide Grade 8 students to see and appreciate the discovery function of proof in geometr