Abstract
The paper reports the contribution of Piero Torasso to the field of Case-Based Reasoning (CBR), with particular attention to the role of CBR in multi-modal diagnostic problem solving. Starting from the idea that CBR could be adopted to focus model-based reasoning during diagnostic problem solving, Piero’s work concentrated on all the different steps of the CBR cycle, namely case retrieval, solution adaptation and case base maintenance. This resulted in the construction of a multi-modal system called
Introduction
In the early 1980s, one of the fathers of AI, Roger Schank introduced the idea of systems able to learn and reason from past experience, by explicitly keeping in their memory the cases they solved, and by exploiting analogical (i.e., by similarity) reasoning to solve new problems. His theory of dynamic memory [28], developed while he was at Yale University, was the basis for the birth of a new discipline later called Case-Based Reasoning (CBR) [1, 26].
Case-Based Reasoning is a problem solving paradigm where new problems (called target or query problems) are solved by retrieving similar problems already solved, possibly by adapting the solutions of such retrieved problems. The main data structure is the case, that is an entity describing a particular problem (in the simplest situation just a collection of 〈feature, value〉 pairs), together with an associated solution. The original idea by Schank and Kolodner was later refined by Aamodt and Plaza [1], resulting in the so-called 4R process, based on four fundamental steps:
The CBR methodology quickly found important applications in tasks like diagnosis [11, 13], planning [10], design [9] and legal reasoning [3].
Piero’s interest in CBR was definitely triggered by three main reasons: the application of the paradigm to diagnostic problem solving; the introduction of a learning component into the diagnostic cycle; the integration of multiple solving paradigms into diagnostic systems.
All the 4R steps were addressed by Piero’s research, with particular attention to the adaptation and retain phases, considered the most challanging ones. Indeed, retrieval and reuse were the phases that received most attention in both the Machine Learning and Information Retrieval communities, even in situations not strictly related to the CBR field (consider for example the so-called “lazy learning” approaches to machine learning [2] and the various implementations of k-NN algorithms and methods [29]).
In the late 80s/early 90s, Piero was hardly working on a formal characterization of diagnostic problems (see the paper by Console et al. in the present issue) and, at the same time, he was one of the most active proponent of the so-called Second Generation Expert Systems architectures [6]. The introduction of a case-based component in a model-based diagnostic system was conceived as a way to address both interests. In Piero’s idea, CBR had to play a role of “lazy learner”, able to deal with potential model incompleteness, as well as to improve computational efficiency. The term multi-modal reasoning becomes a keyword for all the related research activity.
In 1993, the First European Workshop on Case-Based Reasoning took place in Kaiserslautern (Germany) and we presented our first attempt to combine case-based and model-based diagnosis [21]; this was greatly influenced by some previous visits to Kolodner’s group at Georgia Tech and to Peter Szolovitz at MIT. Szolovitz had been the advisor of Phyllis Koton whose thesis’ main result was the
Since
Performance issues related to such multi-modal architectures triggered also the collaboration of Piero with Marteen Van Someren in Amsterdam. They started to consider in more detail the utility problem associated to the caching of cases in the case library, in order to construct a “cost model” of the system that could be used to predict the effect of changes to the system itself, and in particular in the case library [33, 34].
In the same period, we also decided to consider more in details performance aspects related to retrieval; the current trend was to tackle the retrieval problem in CBR by: avoiding an exhaustive search of the (structured) case library, while having the guarantee of retrieving the most similar cases [27]; considering not only the so-called “shallow similarity” among cases, but also the similarity expressed in terms of adaptation effort through the so-called Adaptation-Guided Retrieval [30, 32].
Again by starting from the
The last part of Piero’s reasearch on CBR and multi-modal reasoning was finally devoted to the Retain step, and in particular to the problem of defining suitable approaches to case-base maintanance. In the late 90s and in the first years of 2000, the main focus of the CBR community moved toward the definition of suitable case-base maintenance policies, by recognizing this problem as a major key to success for case-based systems. The utility or “swamping” problem already addressed by Piero in the work on the cost model, was then deeply analyzed in the context of multi-modal problem solving, by identifying three main sources of system performance: response time, quality of the proposed solutions and system competence [25]. The idea of improving system competence by indiscriminately adding new cases in the case library had been already debated and refuted by Smyth and Keane in their IJCAI 95 paper [31], but a principled approach to the problem was still missing, especially in the context of multi-modal CBR. We addressed the problem from the latter perspective, by considering the refinements of different case base maintanance strategies [19, 24]. We finally proposed and compared two different methodologies; the first was a competence-based strategy aimed at replacing a set of stored cases with the current one, if the latter exhibits an estimated competence comparable with the estimated competence of the considered set of stored cases. The second one, called Learning by Failure with Forgetting (LFF), was based on incremental learning of cases interleaved with an off-line processes of case elimination; cases whose usage does not fulfill specific utility conditions are deleted from the case library. The experimental results demonstrated the practical usefulness of both strategies, with respect to the maintenance of suitable performance levels for the target system [20].
The activity of Piero on CBR and multi-modal reasoning had its epilogue in two important contributions. The first is a summary of Piero’s work on multiple representations and multi-modal reasoning in one of the areas where his scientific contribution has been most relevant: medical AI [36]. With his unique capacity of framing a complex and pluriannual activity, he described the motivations for developing medical diagnostic systems exploiting multiple representations and multi-modal reasoning, starting from the early experiences with the system
The last work is the “final” chapter of the
This has been my last work with Piero and definitely one of the most important of my whole scientific career. We can no doubt state that all the issues related to advances in the CBR research have seen a contribution by Piero; also thanks to him, the Italian AI research community had been the opportunity of being suitably represented inside the very dynamic international CBR community.
