Introduction

“The digital camera Profishot is a must-buy for you because…” “In fact, for your requirements as a semiprofessional photographer, you should not use digital cameras of type Lowcheap because…” Such information is commonly exchanged between a salesperson and a customer during in-store recommendation processes and is usually termed an explanation (Brewer et al. 1998).

The concept of explanation is frequently exploited in human communication and reasoning tasks. Consequently, research within artificial intelligence – in particular, into the development of systems that mimic human behavior – has shown great interest in the nature of explanations. Starting with the question, “What is an explanation?”, we are confronted with an almost unlimited number of possibilities.

Explanations such as (1) “The car type Jumbo-Family-Van of brand Rising-Sun would be well suited to your family because you have four children and the car has seven seats”; (2) “The light bulb shines because you turned it on”; (3) “I washed the dishes because my brother did it last time”; or simply (4) “You have to do your homework because your dad said so”, are examples of explanations depending on circumstances and make the construction of a generic approach for producing explanations difficult. The work of Brewer et al. (1998) distinguishes among functional, causal, intentional, and scientific explanations. Functional explanations (such as explanation 1) deal with the functions of systems. Causal explanations (such as explanation 2) provide causal relationships between events.