Exception Handling in Workflow Systems

In this paper, defeasible workflow is proposed as a framework to support exception handling for workflow management. By using the justified ECA rules to capture more contexts in workflow modeling, defeasible workflow uses context dependent reasoning to enhance the exception handling capability of workflow management systems. In particular, this limits possible alternative exception handler candidates in dealing with exceptional situations. Furthermore, a case-based reasoning (CBR) mechanism with integrated human involvement is used to improve the exception handling capabilities. This involves collecting cases to capture experiences in handling exceptions, retrieving similar prior exception handling cases, and reusing the exception handling experiences captured in those cases in new situations.     

基于案例和描述逻辑的海上援救规划方法

实现知识共享和案例检索是开发基于案例的智能决策支持系统面临的首要问题。针对海上援救指挥决策,采用Web本体语言OWL表示危机态势信息和海上援救领域知识,提出一种基于案例推理和描述逻辑推理的援救规划业务流程。以援救搁浅触礁舰船为例,对海损状况及其援救方法等信息进行了描述和推理。实验结果表明,根据具有语义的领域知识使用描述逻辑推理机对危机态势信息进行分类,能事先限定所要搜索的案例库和选择合理的援救方法,并提高案例检索效率和援救规划的实用性。     

An introduction to case-based reasoning

Case-based reasoning means using old experiences to understand and solve new problems. In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem. Case-based reasoning can mean adapting old solutions to meet new demands; using old cases to explain new situations; using old cases to critique new solutions; or reasoning from precedents to interpret a new situation (much like lawyers do) or create an equitable solution to a new problem (much like labor mediators do). This paper discusses the processes involved in case-based reasoning and the tasks for which case-based reasoning is useful.This article is excerpted from Case-Based Reasoning by Janet Kolodner, to be published by Morgan-Kaufmann Publishers, Inc. in 1992.This work was partially funded by darpa under Contract No. F49620-88-C-0058 monitored by AFOSR, by NSF under Grant No. IST-8608362, and by ARI under Contract No. MDA-903-86-C-173.     

Enhancing the performance of an agent-based manufacturing system through learning and forecasting

Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through learning from history (based on distributed case-based learning and reasoning) and learning from the future (through system forecasting simulation). Learning from history is used to enhance coordination capabilities by minimizing communication and processing overheads. Learning from the future is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.     

Introspective reasoning for index refinement in case-based reasoning

Introspective reasoning can enable a reasoner to learn by refining its own reasoning processes. In order to perform this learning, the system must monitor the course of its reasoning to detect learning opportunities and then apply appropriate learning strategies. This article describes lessons learned from research on a computer model of how introspective reasoning can guide failure-driven learning. The computer model monitors its own reasoning by comparing it to a model of the desired behaviour of its reasoning, and learns in response to deviations from the ideal defined by the model. The approach is applied to the problem of determining indices for selecting cases from a case-based planner's memory. Experiments show that learning driven by this introspective reasoning both decreases retrieval effort and improves the quality of plans retrieved, increasing the overall performance of the planning system compared to case learning alone.     

Logic programming and reasoning with incomplete information

The purpose of this paper is to expand the syntax and semantics of logic programs and disjunctive databases to allow for the correct representation of incomplete information in the presence of multiple extensions. The language of logic programs with classical negation, epistemic disjunction, and negation by failure is further expanded by new modal operators K and M (where for the set of rulesT and formulaF, KF stands for F is known to be true by a reasoner with a set of premisesT and MF means F may be believed to be true by the same reasoner). Sets of rules in the extended language will be called epistemic specifications. We will define the semantics of epistemic specifications (which expands the semantics of disjunctive databases from) and demonstrate their applicability to formalization of various forms of commonsense reasoning. In particular, we suggest a new formalization of the closed world assumption which seems to better correspond to the assumption's intuitive meaning.     

Derivational Analogy in PRODIGY: Automating Case Acquisition,Storage, and Utilization

Expertise consists of rapid selection and application of compiled experience. Robust reasoning, however, requires adaptation to new contingencies and intelligent modification of past experience. And novel or creative reasoning, by its real nature, necessitates general problem-solving abilities unconstrained by past behavior. This article presents a comprehensive computational model of analogical (case-based) reasoning that transitions smoothly between case replay, case adaptation, and general problem solving, exploiting and modifying past experience when available and resorting to general problem-solving methods when required. Learning occurs by accumulation of new cases, especially in situations that required extensive problem solving, and by tuning the indexing structure of the memory model to retrieve progressively more appropriate cases. The derivational replay mechanism is discussed in some detail, and extensive results of the first full implementation are presented. These results show up to a large performance improvement in a simple transportation domain for structurally similar problems, and smaller improvements when less strict similarity metrics are used for problems that share partial structure in a process-job planning domain and in an extended version of the strips robot domain.     

Automating case selection in the construction of a case library

An approach to case selection in the construction of a case library is presented in which the most useful case to be added to the library is identified by evaluation of the additional coverage provided by candidate cases. Cases that can be solved by the addition of a candidate case to the library are discovered in the approach by reversing the direction of case-based reasoning. The computational effort required in the evaluation of candidate cases can be reduced by focusing the search on a specified region of the problem space. The approach has been implemented in CaseMaker, an intelligent case-acquisition tool designed to support the authoring process in a case-based reasoner for estimation tasks.     

Object Oriented Case Representation for CBR Application in Structural Analysis

Knowledge representation is an essential element of a problem-solving technique through computational work. This article describes the knowledge representation scheme formulated to represent a problem in the structural analysis domain for solution through case-based reasoning (CBR). The numerical knowledge is extracted from a real-life problem that can be used as an input in a case-based reasoner. The geometric topology, loading, and mesh distribution for structure from a solved problem is represented in the form of numerical values for easy adaptation by the new problem. The representation scheme is a step forward in development of a system to be utilized for the time-consuming structural analysis requiring heavy computational power, such as an aircraft wing and fuselage components. The success of the representation strategy is a proof that CBR can work as a powerful problem-solving tool in this domain.     

Global Navigation in Dynamic Environments Using Case-Based Reasoning

This paper presents a global navigation strategy for autonomous mobile robots in large-scale uncertain environments. The aim of this approach is to minimize collision risk and time delays by adapting to the changes in a dynamic environment. The issue of obstacle avoidance is addressed on the global level. It focuses on a navigation strategy that prevents the robot from facing the situations where it has to avoid obstacles. To model the partially known environment, a grid-based map is used. A modified wave-transform algorithm is described that finds several alternative paths from the start to the goal. Case-based reasoning is used to learn from past experiences and to adapt to the changes in the environment. Learning and adaptation by means of case-based reasoning permits the robot to choose routes that are less risky to follow and lead faster to the goal. The experimental results demonstrate that using case-based reasoning considerably increases the performance of the robot in a difficult uncertain environment. The robot learns to take actions that are more predictable, minimize collision risk and traversal time as well as traveled distances.     

Exception Handling for Conflict Resolution in Cross-Organizational Workflows

Workflow management systems (WfMSs) are being increasingly deployed to deliver e-business transactions across organizational boundaries. To ensure a high service quality in such transactions, exception-handling schemes for conflict resolution are needed. The conflicts primarily arise due to failure of a task in workflow execution because of underlying application, or controlling WfMS component failures or insufficient user input. So far, little progress has been reported in addressing conflict resolution in cross-organizational business processes, though its importance has been recognized. In this paper, we identify the exception handling techniques that support conflict resolution in cross-organizational settings. In particular, we propose a novel, bundled exception-handling approach, which supports (1) exception knowledge sharing--sharing exception specifications and handling experiences, (2) coordinated exception handling, and (3) intelligent problem solving--using case based reasoning to reuse exception handing experiences. A prototype of this exception handling mechanism is developed and integrated as a part of the METEOR Workflow Management System. An evaluation of our approach is also presented through some sample workflow applications.     

Feature Weight Maintenance in Case Bases Using Introspective Learning

A key issue in case-based reasoning is how to maintain the domain knowledge in the face of a changing environment. During the case retrieval process in case-based reasoning, feature-value pairs are used to compute the ranking scores of the cases in a case base, and different feature-value pairs may have different importance measures, represented as weight values, in this computation. How to maintain a set of appropriate feature weights so that they can be used to solve future problems effectively and efficiently will be a key factor in determining the success of case-based reasoning applications.Our focus in this paper is on the dynamic maintenance of feature weights in a case base. We address a particular problem related to the feature-weight maintenance issue. In current practice, the feature weights are assigned and revised manually, not only making them highly informal and inaccurate, but also involving intensive labor. We would like to introduce a semi-automatic introspective learning method to partially address this issue. Our approach is to construct a network architecture on the case base that supports introspective learning. Weight learning and weight-evolution are accomplished in the background through the integration of a learning network into case-based reasoning, in which, while the reasoning part is still case based, the learning part is shouldered by a layered network. The computation in the network follows well-known neural network algorithms with well known properties. We demonstrate the effectiveness of our approach through experiments.     

Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions

Learning in the mobile robot domain is a very challenging task, especially in non-stationary conditions. The behavior-based approach has proven to be useful in making mobile robots work in real-world situations. Since the behaviors are responsible for managing the interactions between the robots and its environment, observing their use can be exploited to model these interactions. In our approach, the robot is initially given a set of behavior-producing modules to choose from, and the algorithm provides a memory-based approach to dynamically adapt the selection of these behaviors according to the history of their use. The approach is validated using a vision- and sonar-based Pioneer I robot in non-stationary conditions, in the context of a multi-robot foraging task. Results show the effectiveness of the approach in taking advantage of any regularities experienced in the world, leading to fas t and adaptable specialization for the learning robot.     

Research Environment for Data Analysis Tool Allocators

Intelligent data analysis implies the reasoned application of autonomous or semi-autonomous tools to data sets drawn from problem domains. Automation of this process of reasoning about analysis (based on factors such as available computational resources, cost of analysis, risk of failure, lessons learned from past errors, and tentative structural models of problem domains) is highly non-trivial. By casting the problem of reasoning about analysis (MetaReasoning) as yet another data analysis problem domain, we have previously [R. Levinson and J. Wilkinson, in Advances in Intelligent Data Analysis, edited by X. Liu, P. Cohen, and M. Berthold, volume LNCS 1280, Springer-Verlag, Berlin, pp. 89–100, 1997] presented a design framework, MetaReasoning for Data Analysis Tool Allocation (MRDATA). Crucial to this framework is the ability of a Tool Allocator to track resource consumption (i.e. processor time and memory usage) by the Tools it employs, as well as the ability to allocate measured quantities of resources to these Tools. In order to test implementations of the MRDATA design, we now implement a Runtime Environment for Data Analysis Tool Allocation, RE:DATA. Tool Allocators run as processes under RE:DATA, are allotted system resources, and may use these resources to run their Tools as spawned sub-processes. We also present designs of native RE:DATA implementations of analysis tools used by MRDATA: K-Nearest Neighbor Tables, Regression Trees, Interruptible (Any-Time) Regression Trees, and Hierarchy Diffusion Temporal Difference Learners. Preliminary results are discussed and techniques for integration with non-native tools are explored.     

Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions

Learning in the mobile robot domain is a very challenging task, especially in nonstationary conditions. The behavior-based approach has proven to be useful in making mobile robots work in real-world situations. Since the behaviors are responsible for managing the interactions between the robots and its environment, observing their use can be exploited to model these interactions. In our approach, the robot is initially given a set of behavior-producing modules to choose from, and the algorithm provides a memory-based approach to dynamically adapt the selection of these behaviors according to the history of their use. The approach is validated using a vision- and sonar-based Pioneer I robot in nonstationary conditions, in the context of a multirobot foraging task. Results show the effectiveness of the approach in taking advantage of any regularities experienced in the world, leading to fast and adaptable specialization for the learning robot.     

Arguments and cases: An inevitable intertwining

We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation. Second, we present,our evolving taxonomy of patterns of actual legal argument. This taxonomy builds upon our much earlier work on argument moves and also on our more recent analysis of how cases are used to support arguments for the interpretation of legal statutes. Third, we show how the theory of argument used by CABARET, a hybrid case-based/rule-based reasoner, can support many of the argument patterns in our taxonomy.This work was supported in part by the National Science Foundation, contract IRI-890841, the Air Force Office of Sponsored Research under contract 90-0359, the Office of Naval Research under a University Research Initiative Grant, contract N00014-87-K-0238, and a grant from GTE Laboratories, Inc., Waltham, Mass.