Proceedings of the KI 2009 Workshop on Complex Cognition

The KI ´09 workshop on Complex Cognition was a joint venture of the Cognition group of the Special Interest Group Artificial Intelligence of the German Computer Science Society (Gesellschaft für Informatik) and the German Cognitive Science Association. Dealing with complexity has become one of the great challenges for modern information societies. To reason and decide, plan and act in complex domains is no longer limited to highly specialized professionals in restricted areas such as medical diagnosis, controlling technical processes, or serious game playing. Complexity has reached everyday life and affects people in such mundane activities as buying a train ticket, investing money, or connecting a home desktop to the internet. Research in cognitive AI can contribute to supporting people navigating through the jungle of everyday reasoning, decision making, planning and acting by providing intelligent support technology. Lessons learned from expert systems research of the nineteen-eighties show that the aim should not be to provide for fully automated systems which can solve specialized tasks autonomously but instead to develop interactive assistant systems where user and system work together by taking advantage of the respective strengths of human and machine. To accomplish a smooth collaboration between humans and intelligent systems, basic research in cognition is a necessary precondition. Insights into cognitive structures and processes underlying successful human reasoning and planning can provide suggestions for algorithm design. Even more important, insights into restrictions and typical errors and misconceptions of the cognitive systems provide information about those parts of a complex task from which the human should be relieved. For successful human-computer interaction in complex domains it has, furthermore, to be decided which information should be presented when, in what way, to the user. We strongly believe that symbolic approaches of AI and psychological research of higher cognition are at the core of success for the endeavor to create intelligent assistant system for complex domains. While insight into the neurological processes of the brain and into the realization of basic processes of perception, attention and senso-motoric coordination are important for the basic understanding of the principles of human intelligence, these processes have a much too fine granularity for the design and realization of interactive systems which must communicate with the user on knowledge level. If human system users are not to be incapacitated by a system, system decisions must be transparent for the user and the system must be able to provide explanations for the reasons of its proposals and recommendations. Therefore, even when some of the underlying algorithms are based on statistical or neuronal approaches, the top-level of such systems must be symbolical and rule-based. The papers presented at this workshop on complex cognition give an inspiring and promising overview of current work in the field which can provide first building stones for our endeavor to create knowledge level intelligent assistant systems for complex domains. The topics cover modelling basic cognitive processes, interfacing subsymbolic and symbolic representations, dealing with continuous time, Bayesian identification of problem solving strategies, linguistically inspired methods for assessing complex cognitive processes and complex domains such as recognition of sketches, predicting changes in stocks, spatial information processing, and coping with critical situations

Deep Learning for Free-Hand Sketch: A Survey

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.Comment: This paper is accepted by IEEE TPAM

Object Recognition and Parsing with Weak Supervision

Object recognition is a fundamental problem in computer vision and has attracted a lot of research attention, while object parsing is equally important for many computer vision tasks but has been less studied. With the recent development of deep neural networks, computer vision researches have been dominated by deep learning approaches, which require large amount of training data for a specific task in a specific domain. The cost of collecting rare samples and making "hard" labels is forbiddingly high and has limited the development of many important vision studies, including object parsing. This dissertation will focus on object recognition and parsing with weak supervision, which tackles the problem when only a limited amount of data or label are available for training deep neural networks in the target domain. The goal is to design more advanced computer vision models with enhanced data efficiency during training and increased robustness to out-of-distribution samples during test. To achieve this goal, I will introduce several strategies, including unsupervised learning of compositional components in deep neural networks, zero/few-shot learning by preserving useful knowledge acquired in pre-training, weakly supervised learning combined with spatial-temporal information in video data, and learning from 3D computer graphics models and synthetic data. Furthermore, I will discuss new findings in our cognitive science projects and explain how the part-based representations benefit the development of visual analogical reasoning models. I believe this series of works alleviates the data-hungry problem of deep neural networks, and improves computer vision models to behave closer to human intelligence

Formalizing graphical notations

The thesis describes research into graphical notations for software engineering, with a principal interest in ways of formalizing them. The research seeks to provide a theoretical basis that will help in designing both notations and the software tools that process them. The work starts from a survey of literature on notation, followed by a review of techniques for formal description and for computational handling of notations. The survey concentrates on collecting views of the benefits and the problems attending notation use in software development; the review covers picture description languages, grammars and tools such as generic editors and visual programming environments. The main problem of notation is found to be a lack of any coherent, rigorous description methods. The current approaches to this problem are analysed as lacking in consensus on syntax specification and also lacking a clear focus on a defined concept of notated expression. To address these deficiencies, the thesis embarks upon an exploration of serniotic, linguistic and logical theory; this culminates in a proposed formalization of serniosis in notations, using categorial model theory as a mathematical foundation. An argument about the structure of sign systems leads to an analysis of notation into a layered system of tractable theories, spanning the gap between expressive pictorial medium and subject domain. This notion of 'tectonic' theory aims to treat both diagrams and formulae together. The research gives details of how syntactic structure can be sketched in a mathematical sense, with examples applying to software development diagrams, offering a new solution to the problem of notation specification. Based on these methods, the thesis discusses directions for resolving the harder problems of supporting notation design, processing and computer-aided generic editing. A number of future research areas are thereby opened up. For practical trial of the ideas, the work proceeds to the development and partial implementation of a system to aid the design of notations and editors. Finally the thesis is evaluated as a contribution to theory in an area which has not attracted a standard approach

Knowledge of knots: shapes in action

Logic is to natural language what knot theory is to natural knots. Logic is concerned with some cognitive performances; in particular, some natural language inferences are captured by various types of calculi (propositional, predicate, modal, deontic, quantum, probabilistic, etc.), which in turn may generate inferences that are arguably beyond natural logic abilities, or non-well synchronized therewith (eg. ex falso quodlibet, material implication). Mathematical knot theory accounts for some abilities - such as recognizing sameness or differences of some knots, and in turn generates a formalism for distinctions that common sense is blind to. Logic has proven useful in linguistics and in accounting for some aspects of reasoning, but which knotting performaces are there, over and beyond some intuitive discriminating abilities, that may require extensions or restrictions of the normative calculus of knots? Are they amenable to mathematical treatment? And what role is played in the game by mental representations? I shall draw from a corpus of techniques and practices to show to what extent compositionality, lexical and normative elements are present in natural knots, with the prospect of formally exploring an area of human competence that interfaces thought, perception and action in a complex fabric

Evaluating the Use of Functional Representations for Ideation in Conceptual Design

Functional representations are often used in the conceptual stages of design because they encourage the designer to focus on the intended use and purpose of a system rather than the physical solution. Function models have been proposed by many researchers as a tool to expand the solution search space and guide concept generation, and many design tools have been created to support function-based design. These tools require designers to create function models of new or existing artifacts, but there is limited published research describing what types of functions should be included in a model or the appropriate level of abstraction to model artifacts. Further, there is little experimental evidence that function models are useful for concept generation. Therefore, this research focuses on how artifacts should be modeled to support ideation in conceptual design. In this research, three functional representations are studied: function models, interaction models, and pruned function models. First, a user study is conducted to test the level of understanding of functional representations by designers. Second, a computational similarity metric is used to identify the appropriate level of abstraction for creating models. Third, a user study is conducted to determine the effects and usefulness of functional representations in concept generation. The three studies show that pruned function models are easier to understand, improve the use of the model by designers, improve the quality of concepts generated, and are more useful for computing functional similarity. Function models contain additional, solution-specific descriptions of functionality that are not useful in conceptual design for ideation, similarity, or interpretation. The interaction model, which is developed in this research, provides a preliminary representation capable of capturing user actions and interactions in addition to artifact functionality, and shows potential for describing non-functional requirements in a manner that is useful to designers. These outcomes serve as a foundation for guidelines for creating conceptual-level models that support ideation in conceptual design

Chador | Veil–Tent Reconceptualizing the veil with a speculative and performative approach

This thesis is a research-creation project that aims to explore the veil beyond the controversial definitions and interpretations which have been ascribed to it, thereby to investigate the borders between inside and outside, men and women, human and non-human. This research, thus, speculates on the possibility of a composed veil, as a customizable boundary that can be personalized and co-created actively by its wearer through a performative approach. While the veil (chador in Farsi) negotiates privacy and interiority as a visible, personal space and boundary constructed directly on the body, at a larger yet still intimate scale, traditional Iranian architecture is also characterized by interiority and introverted spatiality as foundational principles. By positioning the body, the veil and Iranian residential architecture in mutual dynamic interaction, this research seeks to reconceptualize the veil as a ‘microcosmic dwelling place’ which defines an extension of privacy in public and emplaces the body within a context. Consequently, each phase of the research-creation project reveals particular material and spatial aspects of the veil, to constitute a multisensory environment that mutably reconstructs the boundaries between body and space. The composed veil, in this framing, no longer limited to any specific religion, or gender, or cultural context, could be redefined by that wearer as a means of individualized emplacement and engagement in a given social context

Creative problem solving and automated discovery : an analysis of psychological and AI research

Since creativity is the ability to produce something novel and unexpected, it has always fascinated people. Consequently, efforts have been made in AI to invent creative computer programs. At the same time much effort was spent in psychology to analyze the foundations of human creative behaviour. However, until now efforts in AI to produce creative programs have been largely independent from psychological research. In this study, we try to combine both fields of research. First, we give a short summary of the main results of psychological research on creativity. Based on these results we propose a model of the creative process that emphasizes its information processing aspects. Then we describe AI approaches to the implementation of the various components of this model and contrast them with the results of psychological research. As a result we will not only reveal weaknesses of current AI systems hindering them in achieving creativity, but we will also make plausible suggestions - based on psychological research - for overcoming these weaknesses

Creative problem solving and automated discovery : an analysis of psychological and AI research

Since creativity is the ability to produce something novel and unexpected, it has always fascinated people. Consequently, efforts have been made in AI to invent creative computer programs. At the same time much effort was spent in psychology to analyze the foundations of human creative behaviour. However, until now efforts in AI to produce creative programs have been largely independent from psychological research. In this study, we try to combine both fields of research. First, we give a short summary of the main results of psychological research on creativity. Based on these results we propose a model of the creative process that emphasizes its information processing aspects. Then we describe AI approaches to the implementation of the various components of this model and contrast them with the results of psychological research. As a result we will not only reveal weaknesses of current AI systems hindering them in achieving creativity, but we will also make plausible suggestions - based on psychological research - for overcoming these weaknesses