Assumption-based Truth Maintenance Systems Research Articles

Abduction in work-planning problems in complex objects

Assumption-based truth maintenance systems (ATMSs) and basic concepts of operating these systems are described. An ATM-based abduction algorithm (AAA) is developed. A heuristic method for this algorithm is proposed. The experimental results of power-maintenance scheduling justified the algorithm's efficiency.

Abductive inference methods in problems of job planning in complex objects

Abductive inference methods in intelligent systems are considered. A description of assumption-based truth maintenance systems and basic concepts for dealing with assumption-based truth maintenance systems are presented. The following abductive inference algorithms are developed: Assumption-based Truth Maintenance System-based Abduction Algorithm and Implicate Abductive Algorithm using of prime implicates. These algorithms are compared. The results of experiments held for an example of designing schedules for energy storage plants have confirmed the efficiency of the Assumption-based Truth Maintenance System-based Abduction Algorithm.

Heuristic and probabilistic methods for taking efficient readings in diagnostic systems

This paper is devoted to the application of the following three approaches in diagnostic problems: heuristic, based on justifications of predictions of the results of operation of device components; probabilistic, using the structural dependence between components; and combined, with application of the probabilistic method and a heuristics for testing the workability of components. The capabilities of assumption-based truth maintenance systems (the so-called ATMS), which allow one to maintain consistency in DBs, are employed. The methods for choosing the point of the recurrent measurement were tested for a nine-bit device for parity checking. The results of testing have confirmed that the methods using the probabilistic and combined approaches provide the maximum efficiency of the choice of places for taking readings.

Constructing probabilistic ATMSs using extended incidence calculus

This paper discusses the relations between extended incidence calculus and assumption-based truth maintenance systems (ATMSs). We first prove that managing labels for statements (nodes) in an ATMS is equivalent to producing incidence sets of these statements in extended incidence calculus. We then demonstrate that the justification set for a node is functionally equivalent to the implication relation set for the same node in extended incidence calculus. As a consequence, extended incidence calculus can provide justifications for an ATMS, because implication relation sets are discovered by the system automatically. We also show that extended incidence calculus provides a theoretical basis for constructing a probabilistic ATMS by associating proper probability distributions on assumptions. In this way, we can not only produce labels for all nodes in the system, but also calculate the probability of any of such nodes in it. The nogood environments can also be obtained automatically. Therefore, extended incidence calculus and the ATMS are equivalent in carrying out inferences at both the symbolic level and the numerical level. This extends a result due to Laskey and Lehner.

Design support systems for process engineering—II. KBDS: An experimental prototype

KBDS is a prototype design support system for process engineering. It was created as an experimental vehicle to test and develop ideas about the representation of the design process, and to demonstrate how such a representation can be used to support the design activity. Our starting point is a representation of the design process consisting of three spaces: One for design alternatives, another tor design objectives, and a third for models of the design alternatives. These have been implemented ni a prototype system, and their structure, behaviour, and interaction are described. The implementation is based on two powerful techniques: Object-oriented programming, and assumption-based truth maintenance systems (ATMS). The functionality of KBDS is shown through a demonstration based on the sequential development of a separation system and the hierarchical conceptual design of the HD A process. This demonstration shows that the current capabilities of KBDS satisfy many of the require ments of a design support system, and provide a basis for future expansion to satisfy the others

Model-based recognition of anatomical objects from medical images

We present both a high-level symbolic model of the human brain, and a method of using this model to aid in the recognition of objects from medical images. The model is stored as a frame-based semantic network consisting of three coexisting graphs (a spatial adjacency graph, a part hierarchy and an inheritance graph). We propose a method similar to assumption-based truth maintenance systems for the collating and reasoning processes required in the labelling of input images.

USING ATMS TO EFFICIENTLY VERIFY THE TERMINATION OF REWRITE RULE PROGRAMS

Assumption-based truth maintenance systems (ATMS) have become powerful and widely used tools in artificial intelligence problem solvers. In this paper, we apply ATMS to verification of termination of computer programs written as a set of rewrite rules. Compared with the traditional methods based on the ordinary backtracking, our method can greatly improve the overall efficiency by virtue of the ATMS's ability to avoid futile backtracking, rediscovering inferences, and rediscovering contradictions. The originality of our work lies in the practical use of the ATMS in a software engineering problem and in the communication protocol between the termination verifier and the ATMS.

CONSTRAINT-DIRECTED NON-SHARP SEPARATION SEQUENCE DESIGN

Abstract Knowledge-based tools are used to design separation sequences where non-key components are allowed to distribute. Specifically, frame-based, rule-based, and assumption-based truth maintenance systems are used. The design tasks include classification, opportunistic decisions, alternative generation and evaluation, and modification guided by constraints. By applying the appropriate information at each design stage, the design process and final design are easy to understand. The use of non-sharp separators, where non-key components can distribute, agrees with the intuition that separation and blending should be minimized. An apparent lower bound on separation purity is defined. The two lower bounds in a column and the relative volatilities are used in the Fenske equation to calculate the minimum number of stages and to model the non-key component distribution. The minimum number of stages is used opportunistically to generate a partial sequence ordering from which alternatives are produced automatic...

Automatic belief revision in a plausibility-driven design environment

Truth maintenance systems (TMSs) offer the most elaborate approach to the belief revision problem and are thus ideal candidates for automating belief revision during the course of plausibility-driven design evolution. However, the theory of plausible design (TPD) necessitates multivalued belief propagation while conventional TMSs are two-valued (IN/OUT) systems. Due to this mismatch, the encoding of constraint dependency graphs (CDGs) in conventional TMSs requires an exponential number of justifications. A belief revision system for TPD called the theory of plausible design belief revision system (TPD-BRS) is proposed. TPD-BRS uses a modified version of the assumption-based TMS label propagation algorithm in conjunction with a label interpreter to support automated belief revision in TPD efficiently.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

DEFT solutions to final testing

A complete approach to reasoning under uncertainty requires support for both identification of the appropriate hypothesis space and ranking hypotheses based on available evidence. We present a hybrid reasoning scheme that combines symbolic and numerical methods for uncertainty management to provide efficient and effective support for both of these tasks. The hybrid is based on symbolic techniques adapted from assumption-based truth maintenance systems (ATMS), combined with numerical methods adapted from the Dempster/Shafer theory of evidence, as extended in Baldwin's Support Logic Programming system. The hybridization is achieved by viewing an ATMS as a symbolic algebra system for uncertainty calculations. This technique has several major advantages over conventional methods for performing inference with numerical certainty estimates in addition to the ability to dynamically determine hypothesis spaces, including improved management of dependent and partially independent evidence, faster run-time evaluation of propositional certainties, and the ability to query the certainty value of a proposition from multiple perspectives.

QPE: Using assumption-based truth maintenance for qualitative simulation

Efficient qualitative simulators are crucial to continued progress in qualitative physics. Assumption-based truth maintenance systems (ATMS) were developed in part to simplify writing such programs. This paper identifies several abstractions for organizing ATMS-based problem-solvers which are especially useful for envisioning. In particular, we describe the many-worlds database, which avoids complex temporal reference schemes; how to organize problem-solving into justify/assume/interpret cycles which successively construct and extend partial solutions; and closed-world tables, which provide a mechanism for making closed-world assumptions. We sketch the design of the Qualitative Process Engine, QPE, an implementation of Qualitative Process theory, to illustrate the utility of these abstractions. On the basis of our experience in developing QPE and analysing its performance, we draw some general conclusions about the advantages and disadvantages of assumption-based truth maintenance systems.

A hybrid approach to reasoning under uncertainty

A complete approach to reasoning under uncertainty requires support for both identification of the appropriate hypothesis space and ranking hypotheses based on available evidence. We present a hybrid reasoning scheme that combines symbolic and numerical methods for uncertainty management to provide efficient and effective support for both of these tasks. The hybrid is based on symbolic techniques adapted from assumption-based truth maintenance systems (ATMS), combined with numerical methods adapted from the Dempster/Shafer theory of evidence, as extended in Baldwin's Support Logic Programming system. The hybridization is achieved by viewing an ATMS as a symbolic algebra system for uncertainty calculations. This technique has several major advantages over conventional methods for performing inference with numerical certainty estimates in addition to the ability to dynamically determine hypothesis spaces, including improved management of dependent and partially independent evidence, faster run-time evaluation of propositional certainties, and the ability to query the certainty value of a proposition from multiple perspectives.