Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude

A PROBLEM-SOLVER/TMS ARCHITECTURE FOR GENERAL CONSTRAINT SATISFACTION PROBLEMS

Constraints, in various forms, are ubiquitous to design problems. In this paper, we provide a formal characterization of a generalized constraint satisfaction problem (CSP) that can be used to model many types of design/planning problems, and the architecture of an imlemented reasoning system for solving this problem. The architecture includes a truth maintenance system (TMS) which is specifically designed to reason about the relationships expressed in the constraints as a problem solution evolves. The CSP consists of two types of data. The first type of datum corresponds to assignments that are handled by the problem solver, and the second type corresponds to constraint terms handled by the TMS. The dependency network, representing the relationships among constraint terms, is static and generally quite small, depending on the number of constraint terms. Also, justifications are never manipulated (only evaluated). This results in an architecture that makes efficient use of both space and time. The need for efficient TMSs, even though these might deal only with certain classes of problems, is underscored by the fact that general purpose TMSs have often been found to be highly inefficient for solving large problems. We also show how certain instances of the generalized CSP can be formulated as an integer programming problem, special cases of which can be solved efficiently using mathematical (integer) programming techniques.Information Systems Working Papers Serie

Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

An approach to user-directed search in interactive problem solving

This thesis studies some problems which are important in establishing interactive problem solving systems. An interactive problem solving system is characterized by the intensive interaction between the user and the system. In order to converge on a solution which satisfies the user, we present a new problem solving scheme - user-directed search (UDS) - where the solution search is directed in a step-by-step manner by the user. Because of its wide applicability, UDS can be very useful for many practic~l cases. The user-directed problem solving is realized by introducing a particular communication mechanism between the user and the system. This enables a user to guide the solution searching in his most preferred directions. Thus the system can first explore the solutions which are more likely to match the user-desired solution. We have developed UDS using two different approaches. In the first approach, additional deduction rules can be created upon the user's request and/or upon changes in practical environments. For this purpose, we have created, in the user interface, an environment which enables a user to add his new requirements in the form of deduction rules. To improve efficiency, we have used a particular backjump search which can first find, and then backjump to, the point which contradicts the user's new requirements. To establish the dependency for this backjumping, we have used assumption-based truth maintenance systems (ATMS) and KEEworlds in the knowledge engineering environment(KEE). In the second approach, we have introduced particular variable groups. In this approach, the user's new requirements are introduced through a scheme in which the user divides the variable set into several different variable groups. By dividing these variable groups according to his choice, a user can effectively control and instruct the search during the process of problem solving. We have introduced here a scheme which we call proximal minimum (closeness) change. The proximal minimum change ensures that, in the direction specified by the user, a closest solution to the previous one will be found if it actually exists. In another aspect, in order to improve efficiency of solution search on a general basis, we have applied some techniques from Constraint Satisfaction Problems (CSP) in establishing non-CSP expert systems, e.g. rule-based and frame-structured expert systems on KEE. We find that these CSP techniques can be used to improve efficiency by performing consistency checking prior to searching for a solution, which we call pre-processing. This pre-processing is introduced to eliminate a number of variable values which are inconsistent with certain unary and binary constraints. In practical applications, this method can be used to avoid a considerable amount of useless backtracking. We have developed an independent module for applying CSP techniques in general purpose programming in KEE. This CSP module provides KEE with ability to establish more versatile expert systems. Through case studies of the truck dispatching problem and the word puzzle problem, we demonstrate how to achieve UDS and how to implement various techniques which we have presented to improve efficiency in UDS. Some of the advantages of UDS are shown in the case studies

A Comparison of SAT Encodings for Acyclicity of Directed Graphs

Many practical applications require synthesizing directed graphs that satisfy the acyclic constraint along with some side constraints. Several methods have been devised for encoding acyclicity of directed graphs into SAT, each of which is based on a cycle-detecting algorithm. The leaf-elimination encoding (LEE) repeatedly eliminates leaves from the graph, and judges the graph to be acyclic if the graph becomes empty at a certain time. The vertex-elimination encoding (VEE) exploits the property that the cyclicity of the resulting graph produced by the vertex-elimination operation entails the cyclicity of the original graph. While VEE is significantly smaller than the transitive-closure encoding for sparse graphs, it generates prohibitively large encodings for large dense graphs. This paper reports on a comparison study of four SAT encodings for acyclicity of directed graphs, namely, LEE using unary encoding for time variables (LEE-u), LEE using binary encoding for time variables (LEE-b), VEE, and a hybrid encoding which combines LEE-b and VEE. The results show that the hybrid encoding significantly outperforms the others

On the relations between SAT and CSP enumerative algorithms

AbstractWe show the equivalence between the so-called Davis–Putnam procedure (Davis et al., Comm. ACM 5 (1962) 394–397; Davis and Putnam (J. ACM 7 (1960) 201–215)) and the Forward Checking of Haralick and Elliot (Artificial Intelligence 14 (1980) 263–313). Both apply the paradigm choose and propagate in two different formalisms, namely the propositional calculus and the constraint satisfaction problems formalism. They happen to be strictly equivalent as soon as a compatible instantiation order is chosen. This equivalence is shown considering the resolution of the clausal expression of a CSP by the Davis–Putnam procedure

Solving the course scheduling problem by constraint programming and simulated annealing

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2008Includes bibliographical references (leaves: 87-62)Text in English; Abstract: Turkish and Englishix, 80 leavesIn this study it has been tackled the NP-complete problem of academic class scheduling (or timetabling). The aim of this thesis is finding a feasible solution for Computer Engineering Department of İzmir Institute of Technology. Hence, a solution method for course timetabling is presented in this thesis, consisting of two phases: a constraint programming phase to provide an initial solution and a simulated annealing phase with different neighbourhood searching algorithms. When the experimental data are obtained it is noticed that according to problem structure, whether the problem is tightened or loosen constrained, the performance of a hybrid approach can change. These different behaviours of the approach are demonstrated by two different timetabling problem instances. In addition to all these, the neighbourhood searching algorithms used in the simulated annealing technique are tested in different combinations and their performances are presented