Using case-based reasoning to focus model-based diagnostic problem solving

Portinale, Luigi; Torasso, Pietro; Ortalda, Carlo; Giardino, A.

doi:10.1007/3-540-58330-0_97

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Due to the hybrid quantitative and qualitative approach, it is also possible to combine the logics with probabilities. Reference [11] introduces an approach to focus reasoning processes using case-based reasoning methods. The paper does however not use domain knowledge (e.g.…”

Section: Related Workmentioning

confidence: 99%

An intelligent agent model with awareness of workflow progress

Both

Hoogendoorn

Mee³

et al. 2011

Appl Intell

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To support human functioning, ambient intelligent agents require knowledge about the tasks executed by the human. This knowledge includes design-time information like: (i) the goal of a task and (ii) the alternative ways for a human to achieve that goal, as well as run-time information such as the choices made by a human during task execution. In order to provide effective support, the agent must know exactly what steps the human is following. However, if not all steps along the path can be observed, it is possible that the agent cannot uniquely derive which path the human is following. Furthermore, in order to provide timely support, the agent must observe, reason, conclude and support within a limited period of time. To deal with these problems, this paper presents a generic focused reasoning mechanism to enable a guided selection of the path which is most likely followed by the human. This mechanism is based upon knowledge about the human and the workflow to perform the task. In order to come to such an approach, a reasoning mechanism is adopted in combination with the introduction of a new workflow representation, which is utilized to focus the reasoning process in an appropriate manner. The approach is evaluated by means of an extensive case study.

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Section: Related Workmentioning

confidence: 99%

An intelligent agent model with awareness of workflow progress

Both

Hoogendoorn

Mee³

et al. 2011

Appl Intell

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show abstract

“…Leake et al [77] use CBR and introspective reasoning to learn case adaptation. Portinale et al [95] introduce a technique called Pivoting-Based retrieval that exploits a heuristic estimate of the adaptability of a solution determining in this way which cases are worth to retrieve. Purvis and Athalye [97] address the situations in which initially retrieved cases are not easily adaptable, and propose a genetic algorithm to improve case adaptability.…”

Section: Adaptation/evaluationmentioning

confidence: 99%

“…Some examples are the following: JULIA [60], integrating CBR and constraints for design tasks; Aamodt's work on knowledge intensive case-based reasoning [1]; CARS [25] that combines case-based and fuzzy rule-based systems (see section 6.4); CREEK [2] which integrates rules and cases and a top level control strategy to decide whether to activate rules or cases to achieve a goal; CABARET [100], which integrates rule-based and case-based reasoning to facilitate the use of rules containing ill-defined terms; GREBE [29], which also integrates rules and cases; PATDEX/MOLTKE [8] integrating models, cases and compiled knowledge; MoCas [90], combining casebased and model-based reasoning for technical diagnosis applications; the work of Portinale et al [95], who also use a combination of models and cases for diagnosis; QMC [4], which uses a semi-qualitative model to reason about possible effects of differences between cases and about the possible causes of observed problems; IKBALS [127], which integrates rules and cases for intelligent information retrieval; A LA CARTE [87], which uses cases to tune rules in a KBS; BOLERO [79] integrating rule-based reasoning at the domain level with case-based reasoning at the meta-level in such a way that the cases guide the inference process at the domain level, allowing learning of control knowledge by experience (see section 6.2); and NOOS [91], [13], a reflective architecture capable of integrating different inference and learning methods. Karacapilidis et al [67] integrate CBR and argumentation-based reasoning to address group decision making processes.…”

Section: Integration With Other Techniquesmentioning

confidence: 99%

Case-Based Reasoning

Mántaras

2001

Machine Learning and Its Applications

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“…The chapter is organized as follows: next section points out the basic assumptions underlying our work in diagnostic problem solving, section describes the formalism of causal model, section provides a logical characterization of diagnostic problem solving and a sketch of the mechanism for diagnostic problem solving adopted in the model-based component, section describes the approach to multiple representations (initially proposed in 13 ) based on the exploitation of a case memory and the integration of Case-Based Reasoning with Model-Based diagnostic reasoning; in section we nally discuss at what extent the integrated architecture addresses (and solves) the problems mentioned above.…”

Section: Introductionmentioning

confidence: 99%