A compositional method is presented for the verification of multi-agent systems. The advantages of the method are the well-structuredness of the proofs and the reusability of parts of these proofs in relation to reuse of components. The method is illustrated for an example multi-agent system, consisting of cooperative information gathering agents. This application of the verification method results in a formal analysis of pro-activeness and reactiveness of agents, and shows which combinations of pro-activeness and reactiveness in a specific type of information agents lead to a successful cooperation.. 1

. Much of the work on ontologies in AI has focused on describing some aspect of reality: objects, relations, states of affairs, events, and processes in the world. A goal is to make knowledge sharable, by encoding domain knowledge using a standard vocabulary based on the ontology. A parallel attempt at identifying the ontology of problem-solving knowledge has a goal of sharable problem-solving methods. For example, when one is dealing with abductive inference problems, the following are some of the terms that occur in the representation of problem-solving methods: hypotheses, explanatory coverage, evidence, likelihood, plausibility, composite hypothesis, etc. Method ontology is, in good part, goal- and method-specific. Generic Tasks," Heuristic Classification," Task-specific Architectures," Task-method Structures," Inference Structures" and Task Structures" are representative bodies of work in the knowledge-systems area that have focused on domainindependent problem-solving methods. Ho...

this paper, we present a solution to the problem how a team of robotic agents can come up with a hypothesis of what might globally be wrong with the environment in similar situations. Hypotheses should entail the currently observed behavior and provide some kind of "forecast" for those areas that no other member of the team visited so far. As time proceeds the hypothesis will be refined to match the actual situation more closely. With a hypothesis about the condition of the environment, single robots can try to avoid areas that potentially contain obstacles, take the shortest way out of these areas, or instead enter these areas to (dis-)prove the hypothesis dependent on the strategy of the team

. The paper introduces an approach for the specification and verification of knowledge-based systems combining conceptual and formal techniques. We identify four elements of the specification of a knowledge-based system: a task definition, a problem-solving method, a domain model, and an adapter that relates the other elements. We present abstract data types and a variant of dynamic logic as formal means to specify and verify these different elements. As a consequence of our conceptual model we can decompose the overall verification task of the knowledge-based systems into different proof obligations. Each proof obligation deals with a different aspect of the entire system. The use of the conceptual model in specification and verification improves understandability and reduces the effort for both activities. The modularization enables reuse of specifications and proofs. A knowledge-based system can be build by combing and adapting different components. 1 INTRODUCTION During the last ...

The paper provides three novel contributions to knowledge engineering. First, we provide a structured approach for the development and adaptation of problem-solving methods. We start from very generic search strategies with weak data structures and add adapters that refine the states and state transitions of the search process and that add assumptions necessary to link the competence of a method with given problem definitions and domain knowledge. Second, we show how the usability-reusability trade-off of taskspecific versus task-independent problem-solving methods can easily be overcome by the virtual existence of specific methods. Third, we provide the concept of an integrated library combining reusable problem definitions, problem-solving methods, and adapters.

In this paper a compositional verification method for models of knowledge-based systems is introduced. Required properties of the system are formally verified by deriving them from assumptions that themselves are properties of sub-components, which in their turn may be derived from assumptions on sub-sub-components, and so on. The method is based on properties that are formalised in terms of temporal semantics; both static and dynamic properties are covered. The compositional verification method imposes structure on the verification process. By the possibility to focus at one level of abstraction (information and process hiding), compositional verification provides transparency and limits the complexity per level. Since verification proofs are structured in a compositional manner, they can be reused in case of modification of the system. The method is illustrated for a generic model for diagnostic reasoning. Keywords Compositional verification, knowledge-based systems, diagnostic reas...

The World-Wide Web is changing the nature of software development to a distributive plug & play process. This requires a new way of managing software by so-called intelligent software brokers. The aim of the European IBROW3 project is to develop an intelligent brokering service that enables third party knowledge-component reuse through the World-Wide Web. Suppliers provide libraries of knowledge components adhering to some standard, and customers can consult these libraries -- through intelligent brokers -- to configure a knowledge system suited to their needs by selection and adaptation. IBROW3 integrates research on heterogeneous databases, interoperability and web technology with knowledge-system technology and ontologies. The aim is to develop a broker that can handle web requests for classes of knowledge system (e.g. diagnostic systems) by accessing libraries of reusable problem-solving methods on the Web, and selecting, adapting and configuring these methods in accor...

In this paper it is shown how specification of behavioural requirements from informal to formal can be integrated within knowledge engineering. The integration of requirements specification has addressed, in particular: the integration of requirements acquisition and specification with ontology acquisition and specification, the relations between requirements specifications and specifications of task models and problem solving methods, and the relation of requirements specification to verification.

. Problem-solving methods are means to describe the inference process of knowledge-based systems. During the last years, a number of these problemsolving methods have been identified that can be reused for building new systems. However, problem-solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in reusing problem-solving methods, in acquiring domain knowledge, and in defining the problem that can be tackled by the knowledge-based systems. In the paper, we discuss the different roles, assumptions play in the development process of knowledge-based systems and provide a survey of assumptions used by diagnostic problem solving. We show how such assumptions introduce target and bias for goal-driven machine learning and knowledge discovery techniques. 1 INTRODUCTION During the last years, Problem-solving methods (PSMs) have become quit...

In Software Engineering, the functionality of a program is traditionally characterised by pre- and postconditions: if the preconditions are fulfilled then the postconditions are guaranteed to hold, but if the preconditions are not fulfilled, no postconditions are guaranteed at all. In this paper, we study how the functionality of a program is affected when the preconditions are only partially fulfilled. This is particularly important for heuristics AI methods which still function reasonably well (although perhaps suboptimally) under less then ideal preconditions. We introduce a framework for characterising partially fulfilled pre- and postconditions. We also present the proof obligations that must be met when using programs under partially fulfilled preconditions. We show that the classical characterisation of programs can be seen as a special case of our gradual characterisation. We illustrate our framework with two simple diagnostic algorithms which coincide in the classical approach, but which behave differently under gradually relaxed preconditions.