A Reusable Multi-Agent Architecture for Active Intelligent Websites

In this paper a reusable multi-agent architecture for intelligent Websites is presented and illustrated for an electronic department store. The architecture has been designed and implemented using the compositional design method for multi-agent systems DESIRE. The agents within this architecture are based on a generic information broker agent model. It is shown how the architecture can be exploited to design an intelligent Website for insurance, developed in co-operation with the software company Ordina Utopics and an insurance company.     

From systems to logic in the early development of nonmonotonic reasoning

This note describes how the notion of nonmonotonic reasoning emerged in Artificial Intelligence from the mid-1960's to 1980. It gives particular attention to the interplay between three kinds of activities: design of high-level programming systems for AI, design of truth-maintenance systems, and the development of nonmonotonic logics. This was not merely a development from logic to implementation; in several cases there was a development from a system design to a corresponding logic. The article concludes with some reflections on the roles and relationships between logicist theory and system design in AI, and in particular in Knowledge Representation.     

On nonmonotonic reasoning with the method of sweeping presumptions

Reasoning almost always occurs in the face of incomplete information. Such reasoning is nonmonotonic in the sense that conclusions drawn may later be withdrawn when additional information is obtained. There is an active literature on the problem of modeling such nonmonotonic reasoning, yet no category of method-let alone a single method-has been broadly accepted as the right approach. This paper introduces a new method, called sweeping presumptions, for modeling nonmonotonic reasoning. The main goal of the paper is to provide an example-driven, nontechnical introduction to the method of sweeping presumptions, and thereby to make it plausible that sweeping presumptions can usefully be applied to the problems of nonmonotonic reasoning. The paper discusses a representative sample of examples that have appeared in the literature on nonmonotonic reasoning, and discusses them from the point of view of sweeping presumptions.     

Nonmonotonic reasoning as prioritized argumentation

This paper proposes a formalism for nonmonotonic reasoning based on prioritized argumentation. We argue that nonmonotonic reasoning in general can be viewed as selecting monotonic inferences by a simple notion of priority among inference rules. More importantly, these types of constrained inferences can be specified in a knowledge representation language where a theory consists of a collection of rules of first order formulas and a priority among these rules. We recast default reasoning as a form of prioritized argumentation and illustrate how the parameterized formulation of priority may be used to allow various extensions and modifications to default reasoning. We also show that it is possible, but more difficult, to express prioritized argumentation by default logic: Even some particular forms of prioritized argumentation cannot be represented modularly by defaults under the same language     

Compositional reasoning for weighted Markov decision processes

Weighted Markov decision processes (MDPs) have long been used to model quantitative aspects of systems in the presence of uncertainty. However, much of the literature on such MDPs takes a monolithic approach, by modelling a system as a particular MDP; properties of the system are then inferred by analysis of that particular MDP. In contrast in this paper we develop compositional methods for reasoning about weighted MDPs, as a possible basis for compositional reasoning about their quantitative behaviour. In particular we approach these systems from a process algebraic point of view. For these we define a coinductive simulation-based behavioural preorder which is compositional in the sense that it is preserved by structural operators for constructing weighted MDPs from components.     

Consistency-motivated reason maintenance in hypothetical reasoning

We present a general framework for logic-based hypothetical reasoning, which introduces truth maintenance, as a collection of truth tables, to Theorist-like hypothetical reasoning systems. This framework uses full first-order logic, performs nonmonotonic reasoning by maintaining refutable theories, and incrementally identifies and records information (similar to the TMS-like caching mechanism) about inconsistencies and derived implicit knowledge, to improve overall search efficiency. The proposed framework retains the simplicity of hypothetical reasoning and adds the advantage of an incremental dynamic truth maintenance system. Experimental results provide evidence for efficiency advantages of the proposed framework.     

Agent-Based Simulation of Animal Behaviour

In the biological literature on animal behaviour, in addition to real experiments and field studies, also simulation experiments are a useful source of progress. Often specific mathematical modelling techniques are adopted and directly implemented in a programming language. Modelling more complex agent behaviours is less adequate using the usually adopted mathematical modelling techniques. The literature on AI and Agent Technology offers more specific methods to design and implement (also more complex) intelligent agents and agent societies on a conceptual level. One of these methods is the compositional multi-agent system design method DESIRE. In this paper it is shown how (depending on the complexity of the required behaviour) a simulation model for animal behaviour can be designed at a conceptual level in an agent-based manner. Different models are shown for different types of behaviour, varying from purely reactive behaviour to pro-active, social and adaptive behaviour. The compositional design method for multi-agent systems DESIRE and its software environment supports the conceptual and detailed design, and execution of these models. A number of experiments reported in the literature on animal behaviour have been simulated for different agent models.     

A causal approach to nonmonotonic reasoning

We introduce logical formalisms of production and causal inference relations based on input/output logics of Makinson and Van der Torre [J. Philos. Logic 29 (2000) 383–408]. These inference relations will be assigned, however, both standard semantics (giving interpretation to their rules), and natural nonmonotonic semantics based on the principle of explanation closure. The resulting nonmonotonic formalisms will be shown to provide a logical representation of abductive reasoning, and a complete characterization of causal nonmonotonic reasoning from McCain and Turner [Proc. AAAI-97, Providence, RI, 1997, pp. 460–465]. The results of the study suggest production and causal inference as general nonmonotonic formalisms providing an alternative representation for a significant part of nonmonotonic reasoning.     

Automated circular assume-guarantee reasoning

Model checking is a successful approach for verifying hardware and software systems. Despite its success, the technique suffers from the state explosion problem which arises due to the large state space of real-life systems. One solution to the state explosion problem is compositional verification, that aims to decompose the verification of a large system into the more manageable verification of its components. To account for dependencies between components, assume-guarantee reasoning defines rules that break-up the global verification of a system into local verification of individual components, using assumptions about the rest of the system. In recent years, compositional techniques have gained significant successes following a breakthrough in the ability to automate assume-guarantee reasoning. However, automation has been restricted to simple acyclic assume-guarantee rules. In this work, we focus on automating circular assume-guarantee reasoning in which the verification of individual components mutually depends on each other. We use a sound and complete circular assume-guarantee rule and we describe how to automatically build the assumptions needed for using the rule. Our algorithm accumulates joint constraints on the assumptions based on (spurious) counterexamples obtained from checking the premises of the rule, and uses a SAT solver to synthesize minimal assumptions that satisfy these constraints. To the best of our knowledge, our work is the first to fully automate circular assume-guarantee reasoning. We implemented our approach and compared it with established non-circular compositional methods that use learning or SAT-based techniques. The experiments show that the assumptions generated for the circular rule are generally smaller, and on the larger examples, we obtain a significant speedup.     

Using default and causal reasoning in diagnosis

We present a theory of default reasoning that is specifically targeted to causal domains. These domains encompass a wide variety of current applications of default reasoning, but here we concentrate on model-based diagnosis. The theory is unique in that it integrates a formal notion of causality with nonmonotonic reasoning techniques based on default logic and abduction. The main structure of the theory is a default causal net (DCN) representing the causal connections among propositions in the domain. The causal net provides a framework for the two nonmonotonic reasoning techniques of assuming defaults and generating explanations for observations, allowing them to be combined in a principled way.     

Nonmonotonic reasoning,nonmonotonic logics and reasoning about change

In this paper we introduce nonmonotonic reasoning and the attempts at formalizing it using nonmonotonic logics. We examine and compare the best known of these. Despite the difference in motivation and technical construction there are strong similarities between these logics which are confirmed when they are finally shown to have a common basis. Finally we consider using nonmonotonic logics to represent reasoning about change.     

Compositional design and reuse of a generic agent model

This article introduces a formally specified design of a compositional generic agent model (GAM). This agent model abstracts from specific application domains; it provides a unified formal definition of a model for weak agenthood. It can be (re) used as a template or pattern for a large variety of agent types and application-domain types. The model was designed on the basis of experiences in a number of application domains. The compositional development method DESIRE was used to design the agent model GAM at a conceptual and logical level. It serves as a unified, precisely defined, conceptual structure, which can be refined by specialization and instantiation to a large variety of other, more specific agents. To illustrate reuse of this agent model, specialization and instantiation to model cooperative information gathering agents is described in depth. Moreover, it is shown how GAM can be used to describe in a unified and, hence, more comparable manner a large number of agent architectures from the literature.     

Default reasoning and belief revision: A syntax-independent approach

As an important variant of Reiter‘s default logic.Poole(1988) developed a nonmonotonic reasoning framework in the classical first-order language,Brewka and Nebel extended Poole‘s approach in order to enable a representation of priorities between defaults.In this paper a general framework for default reasoning is presented,which can be viewed as a generalization of the three approaches above.It is proved that the syntax-independent default reasoning in this framework is identical to the general belief revision operation introduced by Zhang et al.(1997).This esult provides a solution to the problem whether there is a correspondence between belief revision and default logic for the infinite case .As a by-product,an answer to the the question,raised by Mankinson and Gaerdenfors(1991),is also given about whether there is a counterpart contraciton in nonmonotonic logic.     

A situation-theoretic account of nonmonotonic reasoning

Situation theory in the sense of Barwise and Perry (1983) and nonmonotonic reasoning have been relatively disparate research programs in AI, the former focusing on computational approaches to natural language processing while the latter has been extensively used as a basic architecture for rational agents. The aim of the present paper is to suggest one way that these two approaches might fit together. Specifically, a situation semantics is given for a system of defeasible reasoning.     

Restricted semantics for default reasoning

In nonmonotonic reasoning there are the problems of inconsistency and incoherence in general, and in default reasoning there may be only one trivial extension or no extension in special. We propose the restricted semantics of four-valued logic for default reasoning to resolve the problems of inconsistency and incoherence and in the meantime retain classical extensions in the presence of consistency and coherency. The restricted semantics can maintain both the expressiveness and reasoning ability of default logic. We provide a transformation approach to compute the restricted extensions by reducing them into classical ones.     

A compositional process control model and its application to biochemical processes

A compositional generic process control model is presented which has been applied to control enzymatic biochemical processes. The model has been designed at a conceptual and formal level using the compositional development method DESIRE, and includes processes for analysis, planning and simulation. It integrates qualitative and quantitative techniques. Its application to enzymatic chemical processes is described.     

DR-Prolog: A System for Defeasible Reasoning with Rules and Ontologies on the Semantic Web

Nonmonotonic rule systems are expected to play an important role in the layered development of the semantic Web. Defeasible reasoning is a direction in nonmonotonic reasoning that is based on the use of rules that may be defeated by other rules. It is a simple, but often more efficient approach than other nonmonotonic rule systems for reasoning with incomplete and inconsistent information. This paper reports on the implementation of a system for defeasible reasoning on the Web. The system 1) is syntactically compatible with RuleML, 2) features strict and defeasible rules, priorities, and two kinds of negation, 3) is based on a translation to logic programming with declarative semantics, 4) is flexible and adaptable to different intuitions within defeasible reasoning, and 5) can reason with rules, RDF, RDF Schema, and (parts of) OWL ontologies