This paper proposes an approach for the adaptation of spatial or temporal cases in a case-based reasoning system. Qualitative algebras are used as spatial and temporal knowledge representation languages. The intuition behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras. A temporal example from the cooking domain is given. (The paper on which this extended abstract is based was the recipient of the best paper award of the 2012 International Conference on Case-Based Reasoning.)
Several research work focused on the representation of time within the CBR framework. Most were interested in the analysis or in the prediction of temporal processes (e.g. breakdown or disease diagnosis starting from regular observations or successive events). The temporal aspect is generally taken into account from sequences of events or sometimes from relative or absolute time stamps @cite_3 @cite_9 @cite_8 . Particularly, the problem of temporal adaptation has been given much attention in CBR with a workflow representation @cite_0 . Only a few work @cite_11 @cite_2 adopted a qualitative representation. @cite_2 , cases are represented by temporal graphs and the retrieval step is based on graph matching. @cite_11 , cases are indexed by chronicles and temporal constraints, which are represented with a subset of Allen relations.
@cite_8 @cite_9 @cite_3 @cite_0 @cite_2 @cite_11
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In recent years, several researchers have studied the suitability of CBR to cope with dynamic or continuous or temporal domains. In these domains, the current state depends on the past temporal states. This feature really makes difficult to cope with these domains. This means that classical individual case retrieval is not very accurate, as the dynamic domain is structured in a temporally related stream of cases rather than in single cases. The CBR system solutions should also be dynamic and continuous, and temporal dependencies among cases should be taken into account. This paper proposes a new approach and a new framework to develop temporal CBR systems: Episode-Based Reasoning. It is based on the abstraction of temporal sequences of cases, which are named as episodes. Our preliminary evaluation in the wastewater treatment plants domain shows that Episode-Based Reasoning seems to outperform classical CBR systems. The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Deja Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information. Cases are descriptions of situations limited in time and space. The research reported here introduces a method for representation and reasoning with time-dependent situations, or temporal cases, within a knowledge-intensive CBR framework. Most current CBR methods deal with snapshot cases, descriptions of a world state at a single time stamp. In many timedependent situations, value sets at particular time points are less important than the value changes over some interval of time. Our focus is on prediction problems for avoiding faulty situations. Based on a well-established theory of temporal intervals, we have developed a method for representing temporal cases inside the knowledge-intensive CBR system Creek. The paper presents the theoretical foundation of the method, the representation formalism and basic reasoning algorithms, and an example applied to the prediction of unwanted events in oil well drilling. In this paper, we present our case-based browsing advisor for the Web, called Broadway. Broadway follows a group of users during their navigations and supports an indirect collaboration to recommend Web pages to visit next. Broadway uses case-based reasoning to reuse precise experiences extracted from past navigations with a time-extended situation assessment, i.e. the recommendations are based mainly on the similarity of ordered sequences of past accessed documents. A first experimental evaluation shows that the system improves the information searching task.