Jun 8, 2005 - Qualitative knowledge to support reasoning about cases. Authors; Authors and affiliations ... Integrated Approaches. First Online: 08 June 2005.
Qualitative Knowledge to Support Reasoning About Cases R o b e r t J. A a r t s a n d J u h o R o u s u VTF Biotechnology and Food Research P.O. Box 1500, FIN-02044 VTF, Finland {Robert.Aarts, Juho.Rousu} @vtt.fi
Abstract. Our recipe planner for bioprocesses, Sophist, uses a semi-qualitative model to reason about cases. The model represents qualitative knowledge about the possible effects of differences between cases and about the possible causes of observed problems. Hence, the model is a crucial resource of adaptation knowledge. The model representation has been developed specifically to support CBR tasks. The essential notion in this representation is that of an influence. Representation of domain knowledge in an influence graph and a mapping of case-features onto nodes of such a graph, enable a variety of interesting reasoning tasks. Examples of such task illustrate how qualitative reasoning and case-based reasoning support each other in complex planning tasks. Keywords: qualitative reasoning, planning, domain knowledge.
1. Introduction A feature of all learning systems is the information that is available a priori. The more knowledge is embedded into the system, the more effective the system is expected to be. In planning applications, domain knowledge typically includes representation of possible actions (operations) with e.g., preconditions and effects. Our bioprocess recipe planner Sophist (Aarts & Rousu, 1996) uses a qualitative model to represent a large amount of domain knowledge. This knowledge is used to analyse differences between cases, to construct adaptations, to evaluate suggested plan changes and to explain outcomes. This implementation of a module for Qualitative Modeling in a CBR system (QMC) rises a number of interesting issues and is the subject of this paper. Of course, QMC is not the first attempt to integrate model knowledge effectively. CADET (Sycara et al., 1992) and KRITIK (Batta et aI., 1994) use qualitative models to represent design cases whereas Sophist applications have one global QMC model. SME (Falkenhainer et al., 1989) constructs analogies between two given qualitative models, but does not retrieve those models itself. Approaches based on explanation based reasoning such as CREEK (Aamodt 1994), SWALE (Schank & Leake, 1989) and CHEF (Hammond 1990) were influential to this work. The crucial difference is that QMC is intended for planning in continuous domains where the quantitative values of plan operations are important. CARMA (Hastings et al., 1995) operates in a semi-continuous domain and uses a causal model to aid case retrieval but not for the
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adaptation per se. CASEY (Koton 1990) is another system that uses a qualitative model to compute case features for indexing purposes. This paper describes the Qualitative Modeling framework for CBR (QMC). First the modeling language is introduced and illustrated with a simple model. Then the algorithms that support case-based reasoning are treated. Finally, the advantages and shortcomings of the current framework are discussed, also with respect to similar approaches.
2. Model Representation in QMC In the next two sections QMC is introduced. The framework is designed to support planning in continuous domains, that is the planning problems are given in terms of continuous quantities. Throughout this paper a model of a very simple flight domain is used as an example (see Aarts & Rousu, 1996, for an example of a more realistic application). In this domain planes have to fly a required distance with a particular payload and a given amount of fuel. A single flight is a case with a given plane, payload and amount of fuel. The task of the case-based reasoner is to construct a flight path such that the requested distance is flown. A simple qualitative model of this domain was constructed (see Figure 1) and a handful of cases were conceived. These cases have flight paths consisting of three flight segments: an ascend, a period of level flight and a descend. A ~deterministic system of differential equations was used to simulate the plans.
2.1 QualitativeModeling QMC is loosely based on the Qualitative Process Theory (QPT) as developed by Forbus (1984). As in QPT, QMC represents processes and variables. A process is an activity that may influences the state of a variable. For instance the process c 1 i m b i n g influences the variable a l t i t u d e ; the altitude of a plane will increase if the process is active (a c o u r i e r typeface will be used to indicate a model node). Influences link processes to variables. Not only are variables influenced by processes, but processes may be influenced by variables. For instance, f u e l c o n s u m p t i o n is influenced by the variable f r i c t i o n . In addition, QMC allows variables to be influenced by variables. For instance, altitude influences friction. Influences are monotonic, an influence is either positive, or negative. Unlike in QPT, influences in QMC have weights that can be used to indicate the relative strength of an influence. For instance, f u e l c o n s u m p t i o n is strongly influenced by climbing and typically influenced by descending, where strongly indicates a stronger link than typically. The information in weights can be used to infer that it is more effective to shorten an ascend than it is to quicken a descend when the fuel consumption should be reduced. As in QPT, the activity of processes is conditional, i.e. a process is only active if its conditions are satisfied. In QMC complex conditions can be formed by primitives and rules. Primitives are evaluable expressions, i.e. the case-based reasoner should be able to compute the truth of each primitive used in a QMC model. An example of such a primitive (and a simple condition) is c l i m b r a t e > 0, where the climb rate is
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not a variable of the model, but a case feature 1. Rules simply combine primitives or other rules, through support links. In essence then, a QMC model is a graph of process, variable and rule nodes linked by influences. It is a kind of spreading activation network, with different interpretations of activity for different types of nodes. For a rule the intuitive interpretation is "truth", for a process it is "rate", and for a variable it is "trend". These take off
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