Prototype system for supporting the incremental modelling of vague geometric configurations

In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing learning assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. Controlled computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of controlled computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD

Research in constraint-based layout, visualization, CAD, and related topics : a bibliographical survey

The present work compiles numerous papers in the area of computer-aided design, graphics, layout configuration, and user interfaces in general. There is nearly no conference on graphics, multimedia, and user interfaces that does not include a section on constraint-based graphics; on the other hand most conferences on constraint processing favour applications in graphics. This work of bibliographical pointers may serve as a basis for a detailed and comprehensive survey of this important and challenging field in the intersection of constraint processing and graphics. In order to reach this ambitious aim, and also to keep this study up-to-date, the authors appreciate any comment and update information

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

New directions for Artificial Intelligence (AI) methods in optimum design

Developments and applications of artificial intelligence (AI) methods in the design of structural systems is reviewed. Principal shortcomings in the current approach are emphasized, and the need for some degree of formalism in the development environment for such design tools is underscored. Emphasis is placed on efforts to integrate algorithmic computations in expert systems

An incremental constraint-based approach to support engineering design.

Constraint-based systems are increasingly being used to support the design of products. Several commercial design systems based on constraints allow the geometry of a product to be specified and modified in a more natural and efficient way. However, it is now widely recognised the needs to have a close coupling of geometric constraints (i.e. parallel, tangent, etc) and engineering constraints (Le. performance, costs, weight, etc) to effectively support the preliminary design stages. This is an active research topic which is the subject of this thesis. As the design evolves, the size of the quation set which captures the constraints can get very large depending on the complexity of the product being designed. These constraints are expected to be solved efficiently to guarantee immediate feedback to the designer. Such requirement is also necessary to support constraint-based design within Virtual Environments, where it is necessary to have interactive speed. However, the majority of constraint-based design systems re-satisfy all constraints from scratch after the insertion of a new design constraint. This process is time consuming and therefore hinders interactive design performance when dealing with large constraint sets. This thesis reports research into the investigation of techniques to support interactive constraint-based design. The main focus of this work is on the development of incremental graph-based algorithms for satisfying a coupled set of engineering and geometric constraints. In this research, the design constraints, represented as simultaneous sets of linear and non-linear equations, are stored in a directed graph called Equation Graph. When a new constraint is imposed, local constraint propagation techniques are used to satisfy the constraint and update the current graph solution, incrementally. Constraint cycles are locally identified and satisfied within the Equation Graph. Therefore, these algorithms efiiciently solve large constraint sets to support interactive design. Techniques to support under-constrained geometry are also considered in this research. The concept of soft constraints is introduced to represent the degrees of freedom of the geometric entities. This is used to allow the incremental satisfaction of newly imposed constraints by exploiting under-constrained space. These soft constraints are also used to support direct manipulation of under-constrained geometric entities, enabling the designers to test the kinematic behaviour of the current assembly. A prototype constraint-based design system has been developed to demonstrate the feasibility of these algorithms to support preliminary desig

Animation From Instructions

We believe that computer animation in the form of narrated animated simulations can provide an engaging, effective and flexible medium for instructing agents in the performance of tasks. However, we argue that the only way to achieve the kind of flexibility needed to instruct agents of varying capabilities to perform tasks with varying demands in work places of varying layout is to drive both animation and narration from a common representation that embodies the same conceptualization of tasks and actions as Natural Language itself. To this end, we are exploring the use of Natural Language instructions to drive animated simulations. In this paper, we discuss the relationship between instructions and behavior that underlie our work and the overall structure of our system. We then describe in some what more detail three aspects of the system - the representation used by the Simulator, the operation of the Simulator and the Motion Generators used in the system

Knowledge-based design support and inductive learning

Designing and learning are closely related activities in that design as an ill-structure problem involves identifying the problem of the design as well as finding its solutions. A knowledge-based design support system should support learning by capturing and reusing design knowledge. This thesis addresses two fundamental problems in computational support to design activities: the development of an intelligent design support system architecture and the integration of inductive learning techniques in this architecture.This research is motivated by the belief that (1) the early stage of the design process can be modelled as an incremental learning process in which the structure of a design problem or the product data model of an artefact is developed using inductive learning techniques, and (2) the capability of a knowledge-based design support system can be enhanced by accumulating and storing reusable design product and process information.In order to incorporate inductive learning techniques into a knowledge-based design model and an integrated knowledge-based design support system architecture, the computational techniques for developing a knowledge-based design support system architecture and the role of inductive learning in Al-based design are investigated. This investigation gives a background to the development of an incremental learning model for design suitable for a class of design tasks whose structures are not well known initially.This incremental learning model for design is used as a basis to develop a knowledge-based design support system architecture that can be used as a kernel for knowledge-based design applications. This architecture integrates a number of computational techniques to support the representation and reasoning of design knowledge. In particular, it integrates a blackboard control system with an assumption-based truth maintenance system in an object-oriented environment to support the exploration of multiple design solutions by supporting the exploration and management of design contexts.As an integral part of this knowledge-based design support architecture, a design concept learning system utilising a number of unsupervised inductive learning techniques is developed. This design concept learning system combines concept formation techniques with design heuristics as background knowledge to build a design concept tree from raw data or past design examples. The design concept tree is used as a conceptual structure for the exploration of new designs.The effectiveness of this knowledge-based design support architecture and the design concept learning system is demonstrated through a realistic design domain, the design of small-molecule drugs one of the key tasks of which is to identify a pharmacophore description (the structure of a design problem) from known molecule examples.In this thesis, knowledge-based design and inductive learning techniques are first reviewed. Based on this review, an incremental learning model and an integrated architecture for intelligent design support are presented. The implementation of this architecture and a design concept learning system is then described. The application of the architecture and the design concept learning system in the domain of small-molecule drug design is then discussed. The evaluation of the architecture and the design concept learning system within and beyond this particular domain, and future research directions are finally discussed