Focusing ATMS Problem-Solving: A Formal Approach

The Assumption-based Truth Maintenance System (ATMS) is a general and powerful problem-solving tool in AI. Unfortunately, its generality usually entails a high computational cost. In this paper, we study how a general notion of cost function can be incorporated into the design of an algorithm for focusing the ATMS, called BF-ATMS. The BF-ATMS algorithm explores a search space of size polynomial in the number of assumptions, even for problems which are proven to have exponential size labels. Experimental results indicate significant speedups over the standard ATMS for such problems. In addition to its improved efficiency, the BF-ATMS algorithm retains the multiple-context capability of an ATMS, and the important properties of consistency, minimality, soundness, as well as the property of bounded completeness. The usefulness of the new algorithm is demonstrated by its application to the task of consistency-based diagnosis, where dramatic efficiency improvements, with respect to the standard solution technique, are obtained

Constraint satisfaction for resource management using ATMs: a timetable design support system

Truth Maintenance Systems (TMS) have turned out to be very useful for many kinds of constraint satisfaction problems, for example qualitative reason¬ ing or scheduling. A particularly difficult constraint satisfaction problem, very well known by course organisers in universities is the arrangement of lectures according to teachers, students and department constraints and preferences, so that the problem is solved and everyone is pleased. The proposal of this project was due to both the interest in knowing how to solve such a problem, and the fact that a version of de Kleer ATMS, a very advanced and efficient TMS system, had been built by Peter Ross, and was available in Edinburgh PROLOG. This thesis first outlines some of the reasons why an ATMS is useful for a timetabling problem, how it is used together with PROLOG, in order to produce a system for solving that problem, and how that system works

Reason Maintenance - State of the Art

This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques

A beginner's guide to belief revision and truth maintenance systems

This brief note is intended to familiarize the non-TMS audience with some of the basic ideas surrounding classic TMS's (truth maintenance systems), namely the justification-based TMS and the assumption-based TMS. Topics of further interest include the relation between non-monotonic logics and TMS's, efficiency and search issues, complexity concerns, as well as the variety of TMS systems that have surfaced in the past decade or so. These include probabilistic-based TMS systems, fuzzy TMS systems, tri-valued belief systems, and so on

Beliefs and Conflicts in a Real World Multiagent System

In a real world multiagent system, where the agents are faced with partial, incomplete and intrinsically dynamic knowledge, conflicts are inevitable. Frequently, different agents have goals or beliefs that cannot hold simultaneously. Conflict resolution methodologies have to be adopted to overcome such undesirable occurrences. In this paper we investigate the application of distributed belief revision techniques as the support for conflict resolution in the analysis of the validity of the candidate beams to be produced in the CERN particle accelerators. This CERN multiagent system contains a higher hierarchy agent, the Specialist agent, which makes use of meta-knowledge (on how the conflicting beliefs have been produced by the other agents) in order to detect which beliefs should be abandoned. Upon solving a conflict, the Specialist instructs the involved agents to revise their beliefs accordingly. Conflicts in the problem domain are mapped into conflicting beliefs of the distributed belief revision system, where they can be handled by proven formal methods. This technique builds on well established concepts and combines them in a new way to solve important problems. We find this approach generally applicable in several domains

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

Mining for Useful Association Rules Using the ATMS

Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and more attention from researchers in data mining community. One big concern is with the size of the extracted rule set. Very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a novel ATMS-based method for extracting non-redundant association rules