Multiple Constraint Satisfaction Research Articles

Integrating Case‐Based and Rule‐Based Reasoning to Meet Multiple Design Constraints

Although case‐based reasoning (CBR) was introduced as an alternative to rule‐based reasoning (RBR), there is a growing interest in integrating it with other reasoning paradigms, including RBR. New hybrid approaches are being piloted to achieve new synergies and improve problem‐solving capabilities. In our approach to integration, CBR is used to satisfy multiple numeric constraints, and RBR allows the performance of “what if” analysis needed for creative design.The domain of our investigation is nutritional menu planning. The task of designing nutritious, yet appetizing, menus is one at which human experts consistently outperform computer systems. Tailoring a menu to the needs of an individual requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria.We first constructed and evaluated independent CBR and RBR menu planning systems, then built a hybrid system incorporating the strengths of each system. The hybrid outperforms either single strategy system, designing superior menus, while synergistically providing functionality that neither single strategy system could provide. In this paper, we present our hybrid approach, which has applicability to other design tasks in which both physical constraints and aesthetic criteria must be met.

MICSL: Multiple Iterative Constraint Satisfaction based Learning

MICSL: Multiple Iterative Constraint Satisfaction based Learning

Acquiring Behavioral Rules for Action-Based Robot Environments

We propose a working environment frame for actionbased robots and describe learning for behavioral rule acquisition enabling robots to operate in such an environment. Robot geometric information is expressed in the frame, as the interaction of a potential distribution and robot behavior as potential energy. Potential representation for the multiple constraint satisfaction problem of multiple agents is described, then a simple soccer game situation applied as an example of simulation. In the potential distribution environment, the potential view is observed as sensor input to a robot. The robot decides its own behavior based on this view. To adaptively acquire behavioral rules, a classifier system is applied whose condition is an observed potential view, and a series of condition-action rules is acquired to attain the goal. The effectiveness of the proposed method is demonstrated by computer simulation.

A market-driven approach to teaching software components engineering

The emergence of software component standards and tools for creating software components is leading to an increasing number of software component developers. Traditional software engineering education, however, emphasizes methods for developing large software packages. It is not clear whether such methods are appropriate for developing components. New techniques may be needed to teach the skills necessary for component development. We identify two skills software developers need to successfully develop components, which are not emphasized in traditional software engineering education: (a) uncovering multipledcustomer domain semanticss and (b) making explicit multipledcustomer framework semantics. Both skills are multiple constraint satisfaction problems. We further argue that training students to produce and market components in a simulated software components marketplace – rather than the more conventional “classroom teaching” + “component homework assignments/projects” – is an effective way of teaching such skills. We then describe an environment we created called SofTrade that simulates a components market and allows students to acquire the necessary skills. We provide a detailed case study of how a student componentdproducer team used market feedback to determine domain and framework semantics. We end by discussing the importance of marketddriven approaches for teaching software components engineering and how such approaches fit into existing software engineering curricula.

Analogy, explanation, and education

AbstractThis article applies a theory of analogical thinking as satisfaction of multiple constraints to the use of analogies in instruction. It shows how the strengths of particularly good analogies and the weaknesses of particularly bad ones can be understood in terms of pragmatic, semantic, and structural constraints. These constraints suggest lessons for how analogies can most effectively be used in instruction.