Reuse and Adaptation of Structured Cases

Reuse is central to the CBR problem-solving process, because the primary aim is to reuse previous solutions to solve a new similar problem. Adaptation is an integral part of reuse to deal with modification to a retrieved solution to address differences between the new and retrieved cases. In this thesis, the term ‘reuse’ refers to any of the reuse tasks, that is verbatim copy or adaptation as explained in Section 1.1.1. In broad terms, there are two main categories of reuse: generative and non-generative. Figure 2.1 shows the categories of reuse, their subsets and related citations. Each category carries a distinctive theme

2.2. Reasoning with Problem-Solving Experiences 27

that differentiates it from its alternative. When a reuse technique combines themes across different categories, we label it with only one theme which is most central for simplicity. An important distinction between the two main categories is in the form of knowledge used from retrieved cases. Non-generative reuse techniques are more common and make use of the actual contents in the retrieved solutions which they attempt to modify during reuse. On the other hand, generative reuse techniques attempt to produce a new solution by retracing the method used to obtain the retrieved solution in the context of the new problem. Generative reuse, also known as replay or constructive reuse, is less commonly used primarily due to the overheads attached to storing and reasoning with problem- solution traces. One of the few works in generative CBR reuse involves a search-based approach proposed for configuration tasks (Plaza & Arcos 2002). Solutions in this domain consist of a complex structure of elements which are captured as states; a domain-specific representation of a partially specified solution. A solution to a given problem is therefore generated by searching the space of solutions guided by the retrieved solution. Wilke and Bergmann (1998) also described how this approach might be applied to obtain a suitable configuration for a PC having specified its utility for attributes such as games and music. There are three approaches to non-generative reuse shown in Figure 2.1: verbatim, transformational and compositional. Verbatim reuse includes all CBR systems where no form of adaptation is carried out after retrieval; these are otherwise called retrieve-only systems. In such systems, adaptation is completely left to the human users and most of the earliest CBR systems such as BATTLE PLANNER (Goodman 1989), ARCHIE-2 (Domeshek & Kolodner 1991) and ASK (Ferguson et al. 1992) fall in this category. Trans- formational and compositional reuse both carry out some form of adaptation but their major difference lies in the manner in which they use their retrieved solutions. The trans- formational approach to reuse typically selects the solution from the retrieved best case and attempts to adapt its contents. On the other hand, compositional approach attempts to combine sub-solutions from several similar cases to produce a new solution during adap- tation. Nevertheless, the transformational approach could adapt its best solution using the contents from other similar cases. In this scenario, the line between transformational and

2.2. Reasoning with Problem-Solving Experiences 28

Reuse

Non- Generative Generative

(Constructive/ Replay)

Substitutional

• Hanney & Keane, 1997 • Gonzalez-Calero et al, 1999

(SWALE)

• Gervas et al, 2001, 2004 (ASPERA)

• Craw et al, 2006

• d’Aquin et al, 2007 (CABAMAKA) • Cordier et al, 2008, 2009 (IAKA) • Leake & Powell, 2007, 2008,

2010 (WebAdapt)

Structural

• Smyth & Cunningham, 1993 (Deja Vu) • Leake et al,1995 (DIAL)

• Zhang et al, 2007 • Diaz-Agudo eta al, 2008 • Sugandh et al, 2008 (Darmok)

• Leake & Kendall-Morwick, 2009 (Phala) • Minor et al, 2010

• Rubin & Watson, 2010 (SARTRE)

Verbatim/ Retrieve-only

Transformational • Wilke & Bergmann,

1998

• Plaza & Arcos, 2002 (T-Air, SaxEx)

Compositional

• De Silva Garza & Maher, 1999 (GENCAD) • Arshadi & Badie, 2000 • Ontanon & Plaza, 2010

(Amalgam)

Figure 2.1: Types of Reuse in CBR (Adapted from Wilke and Bergmann, 1998)

compositional reuse becomes blurred. A compositional approach to reuse was proposed for tutoring library system (Arshadi & Badie 2000) where chapters from different textbooks are combined into a new book in response to a user request. The user request consist of a set of keywords and structured information like searching area, user’s current knowledge level and desired status of knowledge while the solutions consist of book chapters, authors and their year of publication. Similar approaches to compositional reuse have also been proposed for use in architectural designs (De Silva Garza & Maher 1999) and formalised in description logic (Ontan´on & Plaza 2010).

The two categories of transformational reuse (substitutional and structural) are quite similar and encompass majority of CBR adaptation techniques. The main distinction has to do with whether there is a change in the overall structure of the retrieved solution or not during adaptation. Substitutional adaptation is commonly used with attribute-value case representations where adaptation only involves modifying the value of attributes. In gen-

2.2. Reasoning with Problem-Solving Experiences 29

eral, CBR adaptation techniques where attribute values are replaced with optimal, better or improved values will fall under this category of substitutional adaptation (Hanney & Keane 1997, Gonz´alez-Calero, G´omez-Albarran & D´ıaz-Agudo 1999, Gerv´as 2001, Gerv´as, D´ıaz-Agudo, Peinado & Herv´as 2004, Craw et al. 2006, D’Aquin, Badra, Lafrogne, Lieber, Napoli & Szathmary 2007, Leake & Powell 2007, Leake & Powell 2008, Cordier, Fuchs, de Carvalho, Lieber & Mille 2008, Badra et al. 2009, Leake & Powell 2010). Structural adaptation approach on the other hand involves re-organization of the solution structure through deletion, insertion and/or substitution operations. It is more suitable for case rep- resentations in domains such as planning and design where case attributes are more com- plex and the sequence of attributes is important for meaningful problem-solving (Smyth & Cunningham 1993, Mu˜noz Avila & Cox 2008, S´anchez-Ruiz, G´omez-Mart´ın, D´ıaz-Agudo & Gonz´alez-Calero 2008, Sugandh et al. 2008, Lee-Urban & Munoz-Avila 2009, Minor, Bergmann, Grg & Walter 2010, Rubin & Watson 2010). However, approaches that can be categorised under structural adaptation has also been applied in several other domains (Leake et al. 1995, Zhang, Louvieris & Petrou 2007, D´ıaz-Agudo, Plaza, Recio-Garc´ıa & Arcos 2008, Leake & Kendall-Morwick 2009).

Previous research in transformational reuse employed general and manually abstracted rules to adapt a past solution given a new problem (Leake et al. 1995). These rules are mined from the case base using differences between previous problems to form antecedent and corresponding solution differences to form adaptation actions as consequence (Hanney & Keane 1997). The use of an adaptation case base has also been explored in trans- forming a previous solution when solving a new problem. Such adaptation case base is used to store a trace of the steps involved when adapting new cases (Leake et al. 1995) or learnt introspectively from the original case base using previous problem differences and corresponding solution differences as an adaptation case (Craw et al. 2006). Self Organising Maps and Neural networks have also been used to learn relations between problem and solution differences in a high dimensional solution space (Zhang et al. 2007). Other techniques that have been proposed for CBR adaptation include genetic algorithm (De Silva Garza & Maher 1999), constraint satisfaction (Purvis & Pu 1995) and revision

2.2. Reasoning with Problem-Solving Experiences 30

theory (Lieber 2007).

Several CBR techniques have been proposed for case adaptation during reuse as dis- cussed in the preceding paragraphs. However, these adaptation/reuse techniques were applied in domains with structured cases and are not directly applicable to textual cases especially when the solution is also textual. The next section discusses relevant works on reuse of textual cases though very few of them incorporate any form of adaptation.