An Artificial Neural Network (ANN) model has been developed for analyzing traffic in an inland waterway network. The main purpose of this paper is to determine how well such a relatively fast model for analyzing a queuing network could substitute for far more expensive simulation. Its substitutability for simulation is judged by relative discrepancies in predicting tow delays between the ANN and simulation models. This model is developed by integrating five distinct ANN submodels that predict tow headway variances at (1) merge points, (2) branching (i.e., diverging) points, (3) lock exits, and (4) link outflow points (e.g., at ports, junctions, or lock entrances), plus (5) tow queuing delays at locks. Preliminary results are shown for those five submodels and for the integrated network analysis model. Eventually, such a network analyzer should be useful for designing, selecting, sequencing, and scheduling lock improvement projects, for controlling lock operations, for system maintenance planning, and for other applications where many combinations of network characteristics must be evaluated. More generally, this method of decomposing complex queuing networks into elements that can be analyzed with ANNs and then recombined provides a promising approach for analyzing other queuing networks (e.g., in transportation, communication, computing, and production systems).

Design space explorers are computer programs that play on an exploration metaphor to support design. They assist designers in creating alternative designs by structuring the process of design creation in a space of alternatives. Subsidiary metaphors relevant to design space explorers are generation, navigation, and reuse. This paper introduces, in two sketches, typed feature structures as a formal system in which a design space explorer and its knowledge level might be implemented. First, informal and abstract properties of typed feature structures suffice to build a sketch of the behavior of a design space explorer. Second, using an example based on single-fronted cottages (a common Australian housing type), we outline the typed feature structure machinery most relevant to design space exploration.

The paper describes a set of seven fundamental principles for achieving “best concurrency and simultaneity.” The concurrent approach is gaining worldwide attention at this moment. The paralleling of life-cycle activities and process restructuring are being deemed necessary by more and more industries. An automobile product development process example is used in this paper to illustrate many aspects of these seven principles. The principles help the concurrent teams, first, to define how to decompose the product, process and work activities and then, how to arrange these decomposed activities so that “best concurrency and simultaneity” can be achieved.

A temporal logic for representing and reasoning on a robotic domain is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The temporal language is a member of the family of Description Logics, which are characterized by high expressivity combined with good computational properties. The logic is used to organize the domain actions and plans in a taxonomy. The classification and recognition tasks, together with the subsumption task form the basis for action management. An action/plan description can be automatically classified into a taxonomy; an action/plan instance can be recognized to take place at a certain moment from the observation of what is happening in the world during a time interval.

Intelligent agents must update their knowledge base as they acquire new information about their environment. The modal logic S5n has been designed for representing knowledge bases in societies of agents. Halpern and Vardi have proposed the notion of refinement of S5n Kripke models in order to solve multi-agent problems in which knowledge evolves. We argue that there are some problems with their proposal and attempt to solve them by moving from Kripke models to their corresponding trees. We define refinement of a tree with a formula, show some properties of the notion, and illustrate with the muddy children puzzle. We show how some diagnosis problems in engineering can be modelled as knowledge-based multi-agent systems, and hence how our approach can address them.

A selective, multiple-criteria method is proposed for handling constraint violations in well-defined design problems. First, the types of design problems upon which the method may apply are presented and a graph topology is adopted for representing the problem decomposition in the level of design parameter interrelations (design parameter graphs). Next, the problem of constraint violations for the design parameters is discussed. It is shown that these violations can be resolved by modifying the values of the primary design parameters and recalculating the values of the violated parameters. Any “blind” attempt of modifying the values of the primary design parameters for resolving occurring violations may, eventually, create additional violations. The proposed method guides the designer toward those primary design parameters that present the least possibility of creating more violations when their values are modified. This is achieved by applying multiple criteria and by producing a final, sorted list of primary design parameters. The designer may then choose the first element of this list to handle efficiently the violations of the design parameters. Two examples are given on a design space where constraint violations occur. Through these examples, the capability of the proposed method in helping the designer to handle constraint violation is shown. The concluding remarks, except for summarizing the potential of the method, determine its boundaries and include a reference on relative work currently under investigation.

In the course of data modelling, many models could be created. Much work has been done on formulating guidelines for model selection. However, by and large, these guidelines are conservative or too specific. Instead of using general guidelines, models could be selected for a particular task based on statistical tests. When selecting one model, others are discarded. Instead of losing potential sources of information, models could be combined to yield better performance. We review the basics of model selection and combination and discuss their differences. Two examples of opportunistic and principled combinations are presented. The first demonstrates that mediocre quality models could be combined to yield significantly better performance. The latter is the main contribution of the paper; it describes and illustrates a novel heuristic approach called the SG(k-NN) ensemble for the generation of good-quality and diverse models that can even improve excellent quality models.

This paper argues from extensive research findings in design psychology and industrial design processes, as well as our own observations, that interactive generative systems can be powerful tools for human designers. Moreover, interactive generative systems can fit naturally into human design thinking and industrial design practice. This discussion is focused on aesthetic design fields like knitwear and graphic design, but is largely applicable to major branches of engineering. Human designers and generative systems have complementary abilities. Humans are extremely good at perceptual evaluation of designs, according to criteria that are extremely hard to program. As a result, they can provide fitness evaluations for evolutionary generative systems. They can also tailor the biases that generation systems use to reach useful solutions quickly. We discuss an application of these approaches: Kelly's evolutionary systems for color scheme design. Automatic design systems can work interactively with human designers by generating complete designs from partial specifications, that can then be used as starting points for designing by modification. We discuss an application of this approach: Eckert's garment shape design system.

In this paper we present a formal common sense theory of the adoption of perception-based beliefs. We begin with a logical analysis of perception and then consider when perception should lead to belief change. Our theory is intended to apply to perception in humans and to perception in artificial agents at the level of the symbolic interface between a vision system and a belief system. In order to provide a context for our work we relate it to the emerging field of cognitive robotics, give an abstract architecture for an agent which is both embodied and capable of reasoning, and relate this to the concrete architectures of two vision-based surveillance systems.

This paper describes a generic approach to guiding designers when making decisions during the early stages of design. The objective of the research is to enable designers to foresee unintended life-cycle consequences during mechanical component design. Engineering design is a process of evolving solutions to a design problem through the commitment of decisions. As a designer commits a new design decision, a more concrete design solution is generated. Decisions made can have intended and unintended consequences on the performance of the life phase activities that follow, such as manufacturing, assembly, and disposal. Many existing tools only consider the impact of the design solution on later life-cycle phases when the solution is almost complete. This makes changes expensive and difficult. This paper presents a novel approach to how consequences encountered in down stream life-cycle phases can be brought to the designer's attention early in generation of component form. For this purpose, a knowledge model has been derived from a phenomena model. The phenomena model describes how life-cycle consequences are generated during component synthesis. An insight into the representation of the resultant knowledge model is discussed through examples. The implementation of a prototype Knowledge Intensive CAD tool, entitled FORESEE, aimed at supporting life-oriented, feature-based component synthesis and exploration, is also described. The results of the evaluation of FORESEE with a range of designers show that by using the system designers are motivated to explore alternative design solutions and are able to make more informed design decisions. This highlights that the knowledge structure provides a base for extending feature-based component design to a ‘Design Synthesis for Multi-X’ approach.

MarCon (Market-based Constraints) applies market-based control to distributed constraint problems. It offers a new approach to distributing constraint problems that avoids challenges faced by current approaches in some problem domains, and it provides a systematic method for applying markets to a wide array of problems. Constraint agents interact with one another via the variable agents in which they have a common interest, using expressions of their preferences over sets of assignments. Each variable integrates this information from the constraints interested in it and provides feedback that enables the constraints to shrink their sets of assignments until they converge on a solution. MarCon originated in a system for supporting human product designers, and its mechanisms are particularly useful for applications integrating human and machine intelligence to explore implicit constraints. MarCon has been tested in the domain of mechanical design, in which its set-narrowing process is particularly useful.