Corpus ID: 7824606

Explanation Goals in Case-Based Reasoning

@inproceedings{Srmo2004ExplanationGI,
  title={Explanation Goals in Case-Based Reasoning},
  author={Frode S{\o}rmo and J. Cassens},
  year={2004}
}
In this paper, we present a short overview of different theories of explanation. We argue that the goals of the user should be taken into account when deciding what is a good explanation for a given CBR system. Some general types relevant to many Case-Based Reasoning (CBR) systems are identified and we use these goals to identify some limitations in using the case as an explanation in CBR systems. 
Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems
TLDR
A prelimenary outline of the combination of two recently proposed classifications of explanations based on the type of the explanation itself and user goals which should be fulfilled is presented. Expand
Goals and Kinds of Explanations in Case-Based Reasoning
TLDR
A prelimenary outline of the combination of two recently proposed classifications of explanations based on the type of the explanation itself and user goals which should be fulfilled is presented. Expand
Generating Explanations using an Automated Planner and Modeling Reasoning Processes, Skills and Knowledge
TLDR
A user focused approach to generating goal oriented explanations that motivate the user to achieve their task and that reflects the way humans’ reason in the context of explanatory dialogue is reflected. Expand
A Case-Based Explanation System for Black-Box Systems
TLDR
This paper presents a Case-Based Reasoning (CBR) solution to providing supporting explanations of black-box systems that uses local information to assess the importance of each feature and takes advantage of the derived feature importance information to help select cases that are a better reflection of the black- box solution and thus more convincing explanations. Expand
Retrieval, reuse, revision and retention in case-based reasoning
TLDR
The cognitive science foundations of CBR and its relationship to analogical reasoning are examined, and a representative selection ofCBR research in the past few decades on aspects of retrieval, reuse, revision and retention are reviewed. Expand
Retrieval, reuse, revision and retention in case-based reasoning
Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessaryExpand
Explainable Distributed Case-Based Support Systems: Patterns for Enhancement and Validation of Design Recommendations
TLDR
It is shown how explanation patterns and contextually enriched explanations of retrieval results can provide human-understandable insights on the system behavior, justify the shown results, and recommend the best cases to be considered for further use. Expand
Gaining insight through case-based explanation
TLDR
A knowledge-light approach to case-based explanations that works by selecting cases based on explanation utility and offering insights into the effects of feature-value differences is examined. Expand
Explainability in human–agent systems
TLDR
This paper presents a taxonomy of explainability in human–agent systems and defines explainability, and its relationship to the related terms of interpretability, transparency, explicitness, and faithfulness. Expand
Evaluating Explainability Methods Intended for Multiple Stakeholders
TLDR
This paper presents an explainability framework formed of a catalogue of explanation methods, and designed to integrate with a range of projects within a telecommunications organisation, and investigates two metrics designed to model the quality of explanations. Expand
...
1
2
3
4
...

References

SHOWING 1-10 OF 25 REFERENCES
Explanation-Driven Case-Based Reasoning
  • A. Aamodt
  • Mathematics, Computer Science
  • EWCBR
  • 1993
TLDR
A generic reasoning method that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning is described. Expand
Combining Case-Based and Model-Based Reasoning for Predicting the Outcome of Legal Cases
TLDR
An algorithm called IBP that combines case-based and model-based reasoning for an interpretive CBR application, predicting the outcome of legal cases and has higher accuracy compared to standard inductive and instance-based learning algorithms. Expand
Interactive Case-Based Reasoning in Sequential Diagnosis
TLDR
It is argued that mixed-initiative dialogue, explanation of reasoning, and sensitivity analysis are essential to meet the needs of experienced as well as novice users in CBR. Expand
Case-based reasoning is a methodology not a technology
  • I. Watson
  • Computer Science
  • Knowl. Based Syst.
  • 1999
TLDR
By describing four applications of case-based reasoning (CBR), that variously use: nearest neighbour, induction, fuzzy logic and SQL, the author shows that CBR is a methodology and not a technology. Expand
Explanations From Intelligent Systems: Theoretical Foundations and Implications for Practice
TLDR
Empirical studies, mainly with knowledge-based systems, are reviewed and linked to a sound theoretical base, which combines a cognitive effort perspective, cognitive learning theory, and Toulmin's model of argumentation. Expand
Goal-Based Explanation Evaluation
TLDR
It is argued that understanding what determines explanations' goodness requires a dynamic theory of evaluation, based on analysis of the information needed to satisfy the many goals that can prompt explanation. Expand
Adaptation-Guided Retrieval: Questioning the Similarity Assumption in Reasoning
TLDR
It is argued that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem, and implemented in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. Expand
The Use of Explanations in Knowledge-Based Systems: Cognitive Perspective and a Process-Tracing Analysis
TLDR
Investigating the nature of explanation use and factors that influence it during users' interaction with a knowledge-based system (KBS) for decision-making offers new insights to why explanations are useful and important, what factors influence explanation use, and what information should be included in explanations. Expand
The Use of Explanations in Knowledge-Based Systems: Cognitive Perspectives and a Process-Tracing Analysis
  • Ji-Ye Mao, Izak Benbasat
  • 2000
This exploratory research investigates the nature of explanation use and factors that influence it during users' interaction with a knowledge-based system (KBS) for decision-making. It draws uponExpand
Strategic induction of decision trees
TLDR
An algorithm for decision-tree induction is presented in which attribute selection is based on the evidence-gathering strategies used by doctors in sequential diagnosis, and an implementation of the algorithm in an environment providing integrated support for incremental learning, problem solving and explanation is presented. Expand
...
1
2
3
...