Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures

Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.Comment: See http://www.jair.org/ for any accompanying file

Experimental study of a similarity metric for retrieving pieces from structured plan cases: its role in the originality of plan case solutions

This paper describes a quantitative similarity metric and its contribution to achieve original plan solutions. This similarity metric is used by an iterative process of piece retrieval from structured plan cases. Within our approach plan cases are tree-like networks of pieces (goals and actions). These case pieces are ill-related each other by links (explanations). These links may be classified as hierarchical or temporal, antecedent or consequent, and explicit or implicit. Besides links, each case piece has also information about its properties (the attributes-value pairs), its hierarchical and temporal position in the case (the address), and about its constraints in the relationship with others (the constraints). The similarity metric computes a similarity value between two case pieces taking into account similarities between these case piece’s information types. Each time a problem is proposed, different weights are given to some of those similarities, with the aim of solving it with an original solution. This similarity metric is used by the system INSPIRER (ImagiNation taking as Source Past and Imperfectly REalated Reasonings). We illustrate the role of the similarity metric in the creativity of solutions, focusing specially their originality, with the presentation of the experimental results obtained in the musical composition domain, which is considered by us as a planning domain

Plans as structured networks of hierarchically and temporally related case pieces

This paper describes a representation of plan cases as a structured set of goals and actions. These goals and actions are the unit pieces that form a case. These case pieces are related each other by hierarchical and temporal links (explanations) forming a tree-like network. We give importance not just to explicit links, i.e., links between case pieces which are concretely known, but also to implicit ones, i.e., possibly unknown links between case pieces. Each case piece is explained by antecedent links and explains other case pieces by consequent links. The retrieval of a case piece is mainly guided by its links and by its surrounding case pieces. Our concept of case piece usefulness is briefly explained. We discuss the benefit of reusing and directly accessing small case pieces from multiple cases for improving the Case-Based Reasoning (CBR) systems’ capability and efficiency to solve problems. We explain the importance of stepwise refinement in plan cases and also the role that temporal representation can take in the meaningful and coherent construction of planning problem solutions. An application in musical composition domain is presented. We also show how a musical composition task can be treated as a planning task

Contributions to Time-bounded Problem Solving Using Knowledge-based Techniques

Time-bounded computations represent major challenge for knowledge-based techniques. Being primarily non-algorithmic in nature, such techniques suffer from obvious open-endedness in the sense that demands on time and other resources for a particular task cannot be predicted in advance. Consequently, efficiency of traditional knowledge-based techniques in solving time-bounded problems is not at all guaranteed. Artificial Intelligence researchers working in real-time problem solving have generally tried to avoid this difficulty by improving the speed of computation (through code optimisation or dedicated hardware) or using heuristics. However, most of these shortcuts are likely to be inappropriate or unsuitable in complicated real-time applications. Consequently, there is a need of more systematic and/or general measures. We propose a two-fold improvement over traditional knowledge-based techniques for tackling this problem. Firstly, that a cache-based architecture should be used in choosing the best alternative approach (when there are two or more) compatible to the time constraints. This cache differs from traditional caches, used in other branches of computer science, in the sense that it can hold not just "ready to use" values but also knowledge suggesting which AI technique will be most suitable to meet a temporal demand in a given context. The second improvement is in processing the cached knowledge itself. We propose a technique which can be called "knowledge interpolation" and which can be applied to different forms of knowledge (such as symbolic values, rules, cases) when the keys used for cache access do not make exact matches with the labels for any cell of the cache. The research reported in this thesis comprises development of cache-based architecture and interpolation techniques, studies of their requisites and representational issues and their complementary roles in achieving time-bounded performance. Ground operations control of an airport and allocating resources for short-wave radio communications are two domains in which our proposed methods are studied

Using features for automated problem solving

We motivate and present an architecture for problem solving where an abstraction layer of "features" plays the key role in determining methods to apply. The system is presented in the context of theorem proving with Isabelle, and we demonstrate how this approach to encoding control knowledge is expressively different to other common techniques. We look closely at two areas where the feature layer may offer benefits to theorem proving — semi-automation and learning — and find strong evidence that in these particular domains, the approach shows compelling promise. The system includes a graphical theorem-proving user interface for Eclipse ProofGeneral and is available from the project web page, http://feasch.heneveld.org

Case Retrieval Nets as a Model for Building Flexible Information Systems

Im Rahmen dieser Arbeit wird das Modell der Case Retrieval Netze vorgestellt, das ein Speichermodell für die Phase des Retrievals beim fallbasierten Schliessen darstellt. Dieses Modell lehnt sich an Assoziativspeicher an, insbesondere wird das Retrieval als Rekonstruktion des Falles betrachtet anstatt als eine Suche im traditionellen Sinne. Zwei der wesentlichen Vorteile des Modells sind Effizienz und Flexibilität: Effizienz beschreibt dabei die Fähigkeit, mit grossen Fallbasen umzugehen und dennoch schnell ein Resultat des Retrievals liefern zu können. Im Rahmen dieser Arbeit wird dieser Aspekt formal untersucht, das Hauptaugenmerk ist aber eher pragmatisch motiviert insofern als der Retrieval-Prozess so schnell sein sollte, dass der Benutzer möglichst keine Wartezeiten in Kauf nehmen muss. Flexibilität betrifft andererseits die allgemeine Anwendbarkeit des Modells in Bezug auf veränderte Aufgabenstellungen, auf alternative Formen der Fallrepräsentation usw. Hierfür wird das Konzept der Informationsvervollständigung diskutiert, welches insbesondere für die Beschreibung von interaktiven Entscheidungsunterstützungssystemen geeignet ist. Traditionelle Problemlöseverfahren, wie etwa Klassifikation oder Diagnose, können als Spezialfälle von Informationsvervollständigung aufgefasst werden. Das formale Modell der Case Retrieval Netze wird im Detail erläutert und dessen Eigenschaften untersucht. Anschliessend werden einige möglich Erweiterungen beschrieben. Neben diesen theoretischen Aspekten bilden Anwendungen, die mit Hilfe des Case Retrieval Netz Modells erstellt wurden, einen weiteren Schwerpunkt. Diese lassen sich in zwei grosse Richtungen einordnen: intelligente Verkaufsunterstützung für Zwecke des E-Commerce sowie Wissensmanagement auf Basis textueller Dokumente, wobei für letzteres der Aspekt der Wiederbenutzung von Problemlösewissen essentiell ist. Für jedes dieser Gebiete wird eine Anwendung im Detail beschrieben, weitere dienen der Illustration und werden nur kurz erläutert. Zuvor wird allgemein beschrieben, welche Aspekte bei Entwurf und Implementierung eines Informationssystems zu beachten sind, welches das Modell der Case Retrieval Netze nutzt.In this thesis, a specific memory structure is presented that has been developed for the retrieval task in Case-Based Reasoning systems, namely Case Retrieval Nets (CRNs). This model borrows from associative memories in that it suggests to interpret case retrieval as a process of re-constructing a stored case rather than searching for it in the traditional sense. Tow major advantages of this model are efficiency and flexibility: Efficiency, on the one hand, is concerned with the ability to handle large case bases and still deliver retrieval results reasonably fast. In this thesis, a formal investigation of efficiency is included but the main focus is set on a more pragmatic view in the sense that retrieval should, in the ideal case, be fast enough such that for the users of a related system no delay will be noticeable. Flexibility, on the other hand, is related to the general applicability of a case memory depending on the type of task to perform, the representation of cases etc. For this, the concept of information completion is discussed which allows to capture the interactive nature of problem solving methods in particular when they are applied within a decision support system environment. As discussed, information completion, thus, covers more specific problem solving types, such as classification and diagnosis. The formal model of CRNs is presented in detail and its properties are investigated. After that, some possible extensions are described. Besides these more theoretical aspects, a further focus is set on applications that have been developed on the basis of the CRN model. Roughly speaking, two areas of applications can be recognized: electronic commerce applications for which Case-Based Reasoning may provide intelligent sales support, and knowledge management based on textual documents where the reuse of problem solving knowledge plays a crucial role. For each of these areas, a single application is described in full detail and further case studies are listed for illustration purposes. Prior to the details of the applications, a more general framework is presented describing the general design and implementation of an information system that makes uses of the model of CRNs

A case-based reasoning approach to improve risk identification in construction projects

Risk management is an important process to enhance the understanding of the project so as to support decision making. Despite well established existing methods, the application of risk management in practice is frequently poor. The reasons for this are investigated as accuracy, complexity, time and cost involved and lack of knowledge sharing. Appropriate risk identification is fundamental for successful risk management. Well known risk identification methods require expert knowledge, hence risk identification depends on the involvement and the sophistication of experts. Subjective judgment and intuition usually from par1t of experts’ decision, and sharing and transferring this knowledge is restricted by the availability of experts. Further, psychological research has showed that people have limitations in coping with complex reasoning. In order to reduce subjectivity and enhance knowledge sharing, artificial intelligence techniques can be utilised. An intelligent system accumulates retrievable knowledge and reasoning in an impartial way so that a commonly acceptable solution can be achieved. Case-based reasoning enables learning from experience, which matches the manner that human experts catch and process information and knowledge in relation to project risks. A case-based risk identification model is developed to facilitate human experts making final decisions. This approach exploits the advantage of knowledge sharing, increasing confidence and efficiency in investment decisions, and enhancing communication among the project participants

Decision support system for emergency management

Pretende-se com este trabalho fornecer um contributo no âmbito do projeto THEMIS, proporcionando um sistema pericial capaz de fornecer um apoio à tomada de decisão. Tendo como base de apoio os casos passados, pretende-se inferir os meios a serem empregues para dar resposta a um certo evento. Este trabalho foi iniciado com a realização de pesquisa bibliográfica, referente aos vários temas que englobam o apoio à tomada de decisão através de sistemas inteligentes. Desta forma, os temas compreendidos nesta tese são: gestão de crises, comando e controlo, tomada de decisão, raciocínio baseado em casos e redes neuronais. Por forma a otimizar-se a resposta do sistema de raciocínio baseado em casos, através de redes neuronais, foi possível entender quais as variáveis que realmente influenciam a inferência dos meios a empregar na resolução dum evento. Possibilitando assim a metodologia de raciocínio baseado em casos suportar o processo da tomada de decisão com uma maior exatidão. A analise dos resultados obtidos, referentes a um evento representativo, mostrou que as variáveis que mais influenciavam a predição do modelo são, ”Entidades Envolvidas”, ”Vitimas Civis” e ”Natureza”. Esta constatação permitiu que o sistema de raciocínio baseado em casos obtivesse resultados muito próximos das tomadas de decisão adotadas pelos comandantes