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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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 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. Temporal action detection in long videos is an important problem. State-of-the-art methods address this problem by applying action classifiers on sliding windows. Although sliding windows may contain an identifiable portion of the actions, they may not necessarily cover the entire action instance, which would lead to inferior performance. We adapt a two-stage temporal action detection pipeline with Cascaded Boundary Regression (CBR) model. Class-agnostic proposals and specific actions are detected respectively in the first and the second stage. CBR uses temporal coordinate regression to refine the temporal boundaries of the sliding windows. The salient aspect of the refinement process is that, inside each stage, the temporal boundaries are adjusted in a cascaded way by feeding the refined windows back to the system for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and achieve state-of-the-art performance on both datasets. The performance gain is especially remarkable under high IoU thresholds, e.g. map@tIoU=0.5 on THUMOS-14 is improved from 19.0 to 31.0 . This article falls under the problem of the symbolic monitoring of real-time complex systems or of video interpretation systems. Among the various techniques used for the on-line monitoring, we are interested here in the temporal scenario recognition. In order to reduce the complexity of the recognition and, consequently, to improve its performance, we explore two methods: the first one is the focus on particular events (in practice, uncommon ones) and the second one is the factorization of common temporal scenarios in order to do a hierarchical recognition. In this article, we present both concepts and merge them to propose a focused hierarchical recognition. This approach merges and generalizes the two main approaches in symbolic recognition of temporal scenarios: the Store Totally Recognized Scenarios (STRS) approach and the Store Partially Recognized Scenarios (SPRS) approach.