Explanation in Case-Based Reasoning–Perspectives and Goals

Self Cite

Abstract. Research on explanation in Case-Based Reasoning (CBR) is a topic that gains momentum. In this context, fundamental issues on what are and to which end do we use explanations have to be reconsidered. This article presents 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. Further on, the contribution of the different knowledge containers for modeling the necessary knowledge is examined.

Section: Explanation Goalsmentioning

confidence: 99%

Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems

Roth–Berghofer

Cassens

2005

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“…The ability to explain itself, its reasoning and actions, has been identified as one core capability of any intelligent entity [2]. The question of what is considered to be a good explanation is context dependent [3], leading to the necessity to design the explanatory capabilities of an ambient intelligent system together with the modelling of the different situations the system is likely to encounter.…”

Section: Introductionmentioning

confidence: 99%

Modelling with Problem Frames: Explanations and Context in Ambient Intelligent Systems

Kofod-Petersen

Cassens

2011

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Abstract. When designing and implementing real world ambient intelligent systems we are in need of applicable information systems engineering methods. The tools we find in the intelligent systems area focus on the knowledge engineering parts, whereas traditional software engineering techniques are usually not designed with the peculiarities of intelligent systems design in mind. This holds in particular for explanation-aware intelligent systems. This work looks at problem frames for explanations and investigates how problem frames can be used to elicit, analyse, and specify these specific requirements. The point of departure is an existing ambient intelligent information system for the hospital ward domain. The work presented here analyses how such a system can be redesigned with a focus on explanation-awareness.

“…But in addition users often need to be educated about the product space, especially if they are to come to understand what is available and why certain options are being recommended by the sales-assistant. Thus recommender systems also need to educate users about the product space: to justify their recommendations and explain the reasoning behind their suggestions; see, for example, [29,36,57,59,60,76,82,95] In summary then, case-based recommendation provides for a powerful and effective form of recommendation that is well suited to many product recommendation scenarios. As a style of recommendation, its use of case knowledge and product similarity, makes particular sense in the context of interactive recommendation scenarios where recommender system and user must collaborative in a flexible and transparent manner.…”

Section: Discussionmentioning

confidence: 99%

Case-Based Recommendation

Smyth

155

Abstract.Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems. IntroductionRecently I wanted to buy a new digital camera. I had a vague idea of what I wanted-a 6 mega-pixel digital SLR from a good manufacturer-but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or navigate through their product catalog by choosing from a series of static features (e.g., manufacturer, camera type, resolution, level of zoom etc.). Each time I selected a feature I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query (e.g. "digital slr, 6 mega-pixels") and presented me with a list of results which I could then browse at my leisure.Both of these access options were helpful in different ways-in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces-however neither provided P. Brusilovsky,