Abstract. We investigate the question of automatic scene construction for visual arts applications. We have implemented a system which can automatically infer a user’s intentions from a partially formed scene, express the inferences as constraints, and then use these to complete the scene. We provide some initial experimental results with the system in order to compare two approaches to constraint-based scene construction. This leads on to a discussion about handing over increasingly meta-level responsibility when building computationally creative systems.

Abstract — In embedded systems, dynamically reconfigurable computing can be partially modified at runtime without stopping the operation of the whole system. In this paper, we consider a reorganization mechanism for dynamically reconfigurable computing in embedded systems to guarantee that invariants of the design are respected. This reorganization is considered as a visual transformation of the logical configuration by the formulated rules. The invariant is recognized under the restructuring of the configuration using reconfiguration rules. Index Terms — Dynamic reconfiguration; Embedded systems; Formal methods; Reconfigurable computing; Software development

Decision support systems should be able to handle representing and reasoning with diagrams, since they are a major component of representations in army situation understanding and planning. Diagrams are useful because desired information about spatial properties of and relations among diagrammatic objects can be perceived from a diagram directly. Spatially relevant changes in a situation or plan can be encoded by modifying a diagram appropriately. In this paper, we present an approach for automating perceptions and actions on a diagram to assist in visual reasoning needed for army decision making. The required perceptions and actions are defined in terms of properties and relations to be satisfied by a set of diagrammatic objects. This paper investigates the use of spatial constraint satisfaction for automatically synthesizing solutions for perceptions and actions. Our research goal is to develop a high-level language that is finite, extensible, humanusable, and expressive enough to describe the properties of desired perceptions and actions as constraints specified in terms of well-defined mathematical/logical functions and predefined perceptions/actions; to compute the solutions of the desired perceptions/actions and diagrammatically represent the outcome for actions; and to automatically synthesize their programs, thereby transforming them into readily executable routines. The ideas are illustrated by several examples. 1.

Abstract. Diagrammatic reasoning is often modeled as a process in which subtasks may be solved, as appropriate, either by inference from symbolic representations or by information extracted by perception from a diagram, and additional subtasks may create or modify objects in the diagram. The required perceptions and actions are defined in terms of properties and relations to be satisfied by a set of diagrammatic objects. Performing such perceptions and actions in general requires visual problem solving for humans. This paper investigates the use of spatial constraint satisfaction for automatically synthesizing solutions for perceptions and actions. Our research goal is to develop a high-level language that is finite, extensible, human-usable, and expressive enough to describe the properties of desired perceptions and actions as constraints specified in terms of well-defined mathematical/logical functions and predefined perceptions/actions; to compute the solutions of the desired perceptions/actions and diagrammatically represent the outcome for actions; and to automatically synthesize their programs, thereby transforming them into readily executable routines. The ideas are illustrated by several examples in different domains. 1

We describe the pictorial representations of infinite ordinals used in teaching set theory, and discuss a possible use in naturalistic foundations of mathematics. 1

In this paper we discuss the importance of good representations in mathematics and relate them to general design issues. Good design makes life easy, bad design difficult. For this reason experienced mathematicians spend a significant amount of their time on the design of their concepts. While many formal systems try to support this by providing a high-level language, we argue that more should be learned from the mathematical practice in order to improve the applicability of formal systems.

We are interested in exploring representation change in mathematics; work by [5] suggests that the structure of metaphor plays an important cognitive role in the development of mathematical theories. In their work, metaphor is taken to involve “grounded, inferencepreserving