Opportunism is an intentional behavior that takes advantage of knowledge asymmetry and results in promoting agents’ own value and demoting others’ value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents’ opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. Agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents’ state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism.

Complex situations and systems can be studied by using adequate models in simulation. An important aspect of models and the simulation software is the ability to use a wide range of possible input parameters. The simulation described in this paper is based on agile manufacturing by using transport robots and cheap reconfigurable production platforms, called equiplets. This setup makes agile manufacturing of different products in parallel possible.The simulation showed the maximum load of such a production environment as well as a proof of concept for the distributed approach for transport used.

Norms are effective and flexible means to control and regulate the behaviour of autonomous systems. Adding norms to a system changes its specification which may in turn ensure desirable system properties. As of yet, there is no generally agreed formal methodology to represent and reason about the dynamics of norms and their impacts on system specifications. In this paper, we introduce various types of norms, such as state-based or action-based norms, and gradually develop a dynamic modal logic to characterize the dynamics of such norms in a formal way. The logic can be used to prove various properties of norm dynamics and their impacts on system specification. Moreover, we show that this logic is sound and complete.

From the article: The term Internet of Things (IoT) is used for situations where one or more devices are connected to a network or possibly the Internet. Most studies focus on the possibilities that arise when a device is capable to share its data with other devices or humans. In this study, the focus is on the device itself and what kind of possibilities an Internet connection gives to the device and its owner or user. Also the data the device needs to participate in a smart way in the IoT are part of this study. Agent technology is the enabling technology for the ideas introduced here. A proof of concept is given, where some concepts proposed in the paper are put into practice.

Personalisation can increase the learning efficacy of educational games by tailoring their content to the needs of the individual learner. This paper presents the Personalised Educational Game Architecture (PEGA). It uses a multi-agent organisation and an ontology to offer learners personalised training in a game environment. The multi-agent organisation's flexibility enables adaptive automation; the instructor can decide to control only parts of the training, while leaving the rest to the intelligent agents.

Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when independencies between variables are involved. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks, the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the correct treatment of statistical evidence is important.

Reasoning about statistics and probabilities can, when not treated with cautiousness, lead to reasoning errors. Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. Uncertainties are by forensic experts often expressed numerically, but lawyers, judges and other legal experts have notorious difficulty interpreting these results [3, 1, 2, 5]. In this demonstration of our main paper [6] we focus on the connection between formal models of argumentation and Bayesian belief networks (BNs). We use BNs because they are a well-known model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph captures the inferences modelled in a Bayesian network but disentangles the complicating graphical properties of such models and instead emphasises its intuitive understanding. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.

This paper describes the implementation of an agile autonomous agent-based manufacturing system based on a grid. This grid contains production machines, represented by agents, capable to perform certain production steps. Products to be made are also represented by agents. Many different products can be made in parallel, each product having its own sequence of production steps. The whole manufacturing is based on interaction of agents living in a distributed environment. This paper explains the basic design considerations and includes a simple example as a proof of concept.

Legal reasoning about evidence can be a precarious exercise, in particular when statistics are involved. A number of recent miscarriages of justice have provoked a scientific interest in formal models of legal evidence. Two such models are presented by Bayesian networks (BNs) and argumentation. A limitation of argumentation is that it is difficult to embed probabilities. BNs, on the other hand, are probabilistic by nature. A disadvantage of BNs is that it can be hard to explain what is modelled and how the results came about. Assuming that a forensic expert presents evidence in a way that is either already a BN or expressed in terms that easily map to a simple BN, we may wish to express the same information in argumentative terms. We address this issue by translating Bayesian networks to arguments. We do this by means of an intermediate structure, called a support graph, which represents the variables from the Bayesian network, maintaining independence information in the network, but connected in a way that more closely resembles argumentation. In the current paper we test the support graph method on a Bayesian network from the literature. We argue that the resulting support graph adequately captures the possible arguments about the represented case. In addition, we highlight strengths and limitations of the method that are revealed by this case study.

Legal reasoning with evidence can be a challenging task. We study the relation between two formal approaches that can aid the construction of legal proof: argumentation and Bayesian networks (BNs). Argument schemes are used to describe recurring patterns in argumentation. Critical questions for many argument schemes have been identified. Due to the increased use of statistical forensic evidence in court it may be advantageous to consider probabilistic models of legal evidence. In this paper we show how argument schemes and critical questions can be modelled in the graphical structure of a Bayesian network. We propose a method that integrates advantages from other methods in the literature.

The purpose of this contribution is to set up a language to evaluate the results of concerted action among interdependent agents against predetermined properties that we can recognize as desirable from a deontic point of view. Unlike the standard view of logics to reason about coalitionally rational action, the capacity of a set of agents to take a rational decision will be restricted to what we will call agreements, which can be seen as solution concepts to a dependence structure present in a certain game. The language will identify those agreements that act accordingly or disaccordingly with the desirable properties arbitrarily set up in the beginning, and will reveal, by logical reasoning, a variety of structural properties of this type of collective action.

This demonstration shows how arguments, formalised in a well defined framework, can be automatically constructed from a given Bayesian network.

Procrastination is a widespread type of self-regulation failure that can have serious negative effects on people's health (e.g., because people delay or omit important health behaviors) and well-being. New “e-coaching” technologies make it possible in principle to offer tailored support to individuals in their efforts to change their self-undermining behavior. In practice, however, such automated support is currently unfeasible because the causal mechanisms behind procrastination are complex and poorly understood. This paper presents a new agent-based model of procrastination that integrates insights from economic models about the dynamics of procrastination with psychological concepts that can help explain the behavior on an individual level. The model is validated by using 5-fold cross validation with simulated annealing to fit and test the parameters on an existing dataset on academic procrastination (n=293). Results show that the agent displays realistic behavior and that the model with the fitted parameters performs significantly better (p<0.01) than the model with randomly selected parameters.

Expressive behaviour is a vital aspect of human interaction. A model for adaptive emotion expression was developed for the Nao robot. The robot has an internal arousal and valence value, which are influenced by the emotional state of its interaction partner and emotional occurrences such as winning a game. It expresses these emotions through its voice, posture, whole body poses, eye colour and gestures. An experiment with 18 children (mean age 9) and two Nao robots was conducted to study the influence of adaptive emotion expression on the interaction behaviour and opinions of children. In a within-subjects design the children played a quiz with both an affective robot using the model for adaptive emotion expression and a non-affective robot without this model. The affective robot reacted to the emotions of the child using the implementation of the model, the emotions of the child were interpreted by a Wizard of Oz. The dependent variables, namely the behaviour and opinions of the children, were measured through video analysis and questionnaires. The results show that children react more expressively and more positively to a robot which adaptively expresses itself than to a robot which does not. The feedback of the children in the questionnaires further suggests that showing emotion through movement is considered a very positive trait for a robot. From their positive reactions we can conclude that children enjoy interacting with a robot which adaptively expresses itself through emotion and gesture more than with a robot which does not do this.

Providing personalized feedback in Intelligent Transport Systems is a powerful tool for instigating a change in driving behaviour and the reduction of CO2emissions. This requires a system that is capable of detecting driver characteristics from real-time vehicle data. In this paper, we apply the architecture and theory of a Neural-Symbolic Cognitive Agent (NSCA) to effectively learn and reason about observed driving behaviour and related driver characteristics. The NSCA architecture combines neural learning and reasoning with symbolic temporal knowledge representation and is capable of encoding background knowledge, learning new hypotheses from observed data, and inferring new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model, and it scales well to hundreds of thousands of data samples as in the application reported in this paper. We have applied the NSCA in an Intelligent Transport System to reduce CO2emissions as part of an European Union project, called EcoDriver. Results reported in this paper show that the NSCA outperforms the state-of-the-art in this application area, and is applicable to very large data.

Recent developments in the forensic sciences have confronted the field of legal reasoning with the new challenge of reasoning under uncertainty. Forensic results come with uncertainty and are described in terms of likelihood ratios and random match probabilities. The legal field is unfamiliar with numerical valuations of evidence, which has led to confusion and in some cases to serious miscarriages of justice. The cases of Lucia de B. in the Netherlands and Sally Clark in the UK are infamous examples where probabilistic reasoning has gone wrong with dramatic consequences. One way of structuring probabilistic information is in Bayesian networks (BNs). In this paper we explore a new method to identify legal arguments in forensic BNs. This establishes a formal connection between probabilistic and argumentative reasoning. Developing such a method is ultimately aimed at supporting legal experts in their decision making process.

Serious games allow for adaptive and personalised forms of training; the nature and timing of learning activities can be tailored to the trainee’s needs and interests. Autonomous game-based training requires for the automatic selection of appropriate exercises for an individual trainee. This paper presents a framework for an automated scenario generation system. The underlying notion is that a learning experience is defined by the objects and agents that inhabit the training environment. Our system uses automated planning to assess the behaviour required to achieve the (personalised) training objective. It then generates a scenario by selecting semantically annotated (or 'smart') objects and by assigning goals to the virtual characters. The resulting situations trigger the trainee to execute the desired behaviour. To test the framework, a prototype has been developed to train the First Aid treatment of burns. Experienced instructors evaluated scenarios written by three types of authors: the prototype, first-aid experts, and laymen. The prototype produced scenarios that were at least as good as laymen scenarios. First-aid experts seemed the best scenario writers, although differences were not significant. It is concluded that combining automated planning, smart objects, and virtual agent behaviour, is a promising approach to automated scenario generation.

This paper describes a concept where products are equipped with agents that will assist in recycling and repairing the product. These so-called product agents represent the product in cyberspace and are capable to negotiate with other products in case of recycling or repair. Some product agents of broken products will offer spare parts, other agents will look for spare parts to repair a broken product. On the average this will enlarge the lifetime of a product and in some cases prevent wasting resources. Apart from reuse of spare parts these agents will also help to locate rare elements in a device, so these elements can be recycled more easily.

Multi-agent systems are viewed as consisting of individual agents whose behaviours are regulated by an organization-oriented normative artefact. This article presents a simplified version of a programming language that is designed to implement normative artefacts. Such artefacts are specified in terms of norms being enforced by monitoring, regimenting and sanctioning mechanisms. The syntax and operational semantics of the programming language are introduced and discussed. A logic is presented that can be used to specify and verify properties of programs developed in this language.

The past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture.

Virtual training systems with intelligent agents provide an effective means to train people for complex, dynamic tasks like crisis management or firefighting. For successful training, intelligent virtual agents should be able to show believable behav

We present a user-friendly decision tree generating algorithm which searches for the best tree from a forest of n trees. We have biased our algorithm to create bigger trees, as they provide insight in the dataset's underlying structure. The algorithm was applied to data from 130 patients, who suffer from an hereditary form of cancer: MEN1. The best of multiple trees was picked based on performance that was evaluated by the algorithm, not only based on classification results of the validation test set, but also on the stability of the train- and validation test set accuracies, discriminative power, tree-width and tree-depth. We present a tree with performance 0.912 on a 0–1 scale, that closely resembles the decision to perform surgery in MEN1 patients by the physician. We also show that even good trees can make medically flawed decisions; that is why they must always be evaluated by health care professionals.

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a difficult task. However, such integration may lead to a better understanding, use and construction of more realistic models. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable for online learning and reasoning. Currently, controlled environments like training simulators do not effectively integrate learning and reasoning. In particular, higher-order concepts and cognitive abilities have many unknown temporal relations with the data, making it impossible to represent such relationships by hand. We introduce a novel cognitive agent model and architecture for online learning and reasoning that seeks to effectively represent, learn and reason in complex training environments. The agent architecture of the model combines neural learning with symbolic knowledge representation. It is capable of learning new hypotheses from observed data, and infer new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model. The validation of the model on real-time simulations and the results presented here indicate the promise of the approach when performing online learning and reasoning in real-world scenarios, with possible applications in a range of areas.

This paper describes an agent-based software infrastructure for agile industrial production. This production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of different products in parallel. The multi-agent-based underlying systems uses two kinds of agents: an agent representing the product and an agent representing the equiplet.

We present a family of sound and complete logics for reasoning about deliberation strategies for SimpleAPL programs. SimpleAPL is a fragment of the agent programming language 3APL designed for the implementation of cognitive agents with beliefs, goals and plans. The logics are variants of PDL, and allow us to prove safety and liveness properties of SimpleAPL agent programs under different deliberation strategies. We show how to axiomatise different deliberation strategies for SimpleAPL programs, and, for each strategy we prove a correspondence between the operational semantics of SimpleAPL and the models of the corresponding logic. We illustrate the utility of our approach with an example in which we show how to verify correctness properties for a simple agent program under different deliberation strategies.

In situations where the behavior of a system must be interpreted because its state is not accessible, it is useful to explain observed behavior in mentalistic terms. This paper presents a formalism based on propositional dynamic logic to model ascription of beliefs, goals, or plans on grounds of observed actions. The formalism is used to provide semantics for an existing approach to abducing the mental state of an observed agent; in doing so it is shown how behavior-producing rules can be given different explanatory interpretations.

Aim of this work is to provide a formal characterization of those emotions that deal with normative reasoning, such as shame and sense of guilt, to understand their relation with rational action and to ground their formalization on a cognitive science perspective. In order to do this we need to identify the factors that constitute the preconditions and trigger the reactions of shame and sense of guilt in cognitive agents, that is when agents feel ashamed or guilty and what agents do when they feel so. We will also investigate how agents can induce and silence these feelings in themselves, i.e. the analysis of defensive strategies they can employ. We will argue that agents do have control over their emotions and we will analyze some operations they can carry out on them.

This paper discusses the role of emotions in artificial agent design and implementation. The syntax and semantics of a simplified version of a logic-based agent-oriented programming language is presented. This programming language facilitates the implementation of artificial agents with emotions. Four types of emotions are distinguished: happiness, sadness, anger, and fear. These emotions are defined relative to agent's goals and plans. The emotions result from the agent's deliberation process and influence the deliberation process. The semantics of each emotion type is incorporated in the transition semantics of the presented agent-oriented programming language. © 2010 Wiley Periodicals, Inc.

An important problem in agent verification is a lack of proper understanding of the relation between agent programs on the one hand and agent logics on the other. Understanding this relation would help to establish that an agent programming language is both conceptually well-founded and well-behaved, as well as yield a way to reason about agent programs by means of agent logics. As a step toward bridging this gap, we study several issues that need to be resolved in order to establish a precise mathematical relation between a modal agent logic and an agent programming language specified by means of an operational semantics . In this paper, we present an agent programming theory that provides both an agent programming language as well as a corresponding agent verification logic to verify agent programs. The theory is developed in stages to show, first, how a modal semantics can be grounded in a state-based semantics, and, second, how denotational semantics can be used to define the mathematical relation connecting the logic and agent programming language. Additionally, it is shown how to integrate declarative goals and add precompiled plans to the programming theory. In particular, we discuss the use of the concept of higher-order goals in our theory. Other issues such as a complete axiomatization and the complexity of decision procedures for the verification logic are not the focus of this paper and remain for future investigation.

This paper addresses the notion of (declarative) goals as used in agent programming. Goals describe desirable states, and semantics of these goals in an agent programming context can be defined in various ways. We focus in this paper on the representation of conflicting goals. In particular, we define two semantics for goals, one for unconditional goals and one for conditional goals. The first is based on propositional logic, and the latter is based on default logic. We establish relations between and properties of these semantics.

In this paper we provide and discuss a language to talk about coherence, a property of interaction that ensures players' abilities non to contradict one other and the empty coalition not to make active choices. With this property we can model a closed-world interaction, such as those of a Coordination Game or of a Prisoner Dilemma, where all the outcomes are determined only by the choices of the agents that are present.

When constructing a formal model of emotions for intelligent agents, two types of aspects have to be taken into account. First, qualitative aspects pertain to the conditions that elicit emotions. Second, quantitative aspects pertain to the actual experience and intensity of elicited emotions. In this paper, we show how the qualitative aspects of a well-known psychological model of human emotions can be formalized in an agent specification language and how its quantitative aspects can be integrated into this model. Furthermore, we discuss several unspecified details and implicit assumptions in the psychological model that are explicated by this effort.

Autonomous agents are believed to have control over their internal state and over their behaviour. For that reason, an agent should control how and by whom it is being influenced. We introduce a reasoning component for BDI-agents that deals with the control over external influences, and we propose heuristics using local knowledge to process incoming stimuli. One of those heuristics is based on information relevance with respect to the agent's current plans and goals. We have developed a way to determine the relevance of information in BDI-agents using magic sets from database research as basis. The method presented shows a new application of magic sets by applying the theory in agent systems.

In this article we discuss the role of emotions in artificial agent design, and the use of logic in reasoning about the emotional or affective states an agent can reside in. We do so by extending the KARO framework for reasoning about rational agents appropriately. In particular, we formalize in this framework how emotions are related to the action monitoring capabilities of an agent. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 601–619, 2006.

The article investigates the logic underlying statements of the form ‘X counts as Y in context C’ which are commonly considered to represent the paradigmatic syntax of constitutive rules, i.e. the non-regulative component of normative systems. The analytical thesis backing the whole work consists in interpreting such statements as contextual classifications. This reading of counts-as is thoroughly investigated in two variants which we call the contextual classificatory reading and the proper contextual classificatory reading. The formal analysis of these readings, which we carry out making use of modal logic, disentangles two possible senses in which counts-as statements can be interpreted within a classificatory perspective, and clarifies the logical relations holding between them. The proposal is then compared in detail with previous work on the topic, in order to shed light on similarities, differences and their grounds.

In this paper, we present a dynamic logic for a propositional version of the agent programming language 3APL. A 3APL agent has beliefs and a plan. The execution of a plan changes an agent's beliefs. Plans can be revised during execution by means of plan revision rules. Due to these plan revision capabilities of 3APL agents, plans cannot be analyzed by structural induction as in for example standard propositional dynamic logic. We propose a dynamic logic that is tailored to handle the plan revision aspect of 3APL. The logic is one for plans that are restricted in a certain way. For this logic, we give a sound and complete axiomatization. Further, we discuss how this logic for restricted 3APL plans can be extended to a logic for non-restricted plans and we discuss some example proofs, using the logic. Finally, we consider the relation between proving properties of 3APL agents and proving properties of procedural programs.

The work investigates the logic underlying the representation of the non-regulative component of normative systems, the so-called counts-as. The analytic thesis we hold here is to view counts-as statements as statements which yield classifications and which hold only with respect to a context. These two aspects of the semantics of counts-as-the classificatory flavor, and the contextual character-are then investigated by means of modal logic techniques from a semantics-driven perspective, and a formalization of counts-as statements is thus proposed. The result is then compared in detail with previous work on the topic, and related with work which, despite developed in different areas of applied and philosophical logic, shares interesting technical and theoretical similarities with our proposal.

In this article we show how formal semantics can be given to reasoning processes in meta-level architectures that reason about (object level) knowledge states and effects changes may have on them. Especially, attention is focused on the upward and downward reflections in these architectures. Temporalized epistemic logic is used to specify meta-level reasoning processes and the outcomes of these. © 2003 Wiley Periodicals, Inc.

This paper proposes two methods for realising automated reasoning about agent-based systems. The framework for modelling intelligent agent behaviour that we focus on is a core of KARO logic, an expressive combination of various modal logics including propositional dynamic logic, a modal logic of knowledge, a modal logic of wishes, and additional non-standard operators. The first method we present is based on a translation of core KARO logic to first-order logic combined with first-order resolution. The second method uses an embedding of core KARO logic into a combination of branching-time temporal logic CTL and multi-modal S5 plus a clausal resolution calculus for these combined logics. We discuss the advantages and shortcomings of each approach and suggest ways to extend each variant to cover more of the KARO framework.

In this paper, we consider communication between agents that employ different vocabularies to represent information. In particular, we develop a communication mechanism in which translators between the vocabularies of agents are generated. Instead of being defined in advance, these translators are dynamically constructed during execution of the system, and are based both on the information that the agents exchange and on their underpinning ontologies. Moreover, these translators are not necessarily defined for the total vocabulary of the agents, but instead, only for the parts that have been involved in communication steps. The framework can for instance be used to study and to analyze experiments as performed in the research on the origins of language, like language games, in which the purpose of communication is to come to a mutual understanding of the agents' vocabularies. © 2001 John Wiley & Sons, Inc.

In this paper, we present a logical framework that combines modality with a first‐order variable‐binding mechanism. The logic, which belongs to the family of hybrid languages, differs from standard first‐order modal logics in that quantification is not performed inside the worlds of a model, but the worlds in the model themselves constitute the domain of quantification. The locality principle of modal logic is preserved via the condition that in each world, the domain of quantification is given by a subset of the entire set of worlds in the model. In comparison with standard hybrid languages, the logic covers separate mechanisms for navigation and for variable‐binding and formalizes reasoning about the worlds of a model in terms of equational logic. We show that the logic is semantically characterized by a generalization of classical bisimulation, called history‐based bisimulation, and study the application of the logic to describe and reason about network topologies.

We present a formal system to reason about and specify the behavior of multiple intelligent artificial agents. Essentially, each agent can perform certain actions, and it may possess a variety of information in order to reason about its and other agent's actions. Thus, our KARO-framework tries to deal formally with the notion of Knowledge, possessed by the agents, and their possible execution of actions. In particular, each agent may reason about its —or, alternatively, other's— Abilities to perform certain actions, the possible Results of such an execution and the availability of the Opportunities to take a particular action. Formally, we combine dynamic and epistemic logic into one modal system, and add the notion of ability to it. We demonstrate that there are several options to define the ability to perform a sequentially composed action, and we outline several properties under two alternative choices. Also, the agents' views on the correctness and feasibility of their plans are highlighted. Finally, the complications in the completeness proof for both systems indicate that the presence of abilities in the logic makes the use of infinite proof rules useful, if not inevitable.

The concept of informational independence plays a key role in most knowledge-based systems. J. Pearl and his co-researchers analysed the basic properties of the concept and formulated an axiomatic system for informational independence. This axiomatic system focuses on independences among mutually disjoint sets of variables. We show that in the context of probabilistic independence a focus on disjoint sets of variables can hide various interesting properties. To capture these properties, we enhance Pearl's axiomatic system with two additional axioms. We investigate the set of models of the thus enhanced system and show that it provides a better characterization of the concept of probabilistic independence than Pearl's system does. In addition, we observe that both Pearl's axiomatic system and our enhanced system offer inference rules for deriving new independences from an initial set of independence statements and as such allow for a normal form for representing independence. We address the normal forms ensuing from the two axiomatic systems for informational independence. © 1998 John Wiley & Sons, Inc.13: 83–109, 1998

In this paper we indicate how default logic can be based on epistemic logic, and particularly how we may employ Halpern & Moses' minimal epistemic states for this purpose. In this way we obtain a simple and natural S5-based logic for default reasoning that appears to be cumulative in the sense of Kraus, Lehmann and Magidor.

Abstract The use of logic in database theory is commonly restricted to the specification of database states. Reasoning about state changes (the database updates) must then be done outside the logic. In this report, we consider a logic that also takes the updates into account. Taking propostional dynamic logic as a starting point, we define PDDL: prepositional dynamic database logic. The main features of PDDL are: •There are two kinds of atomic updates in PDDL, passive and active updates. Passive updates just change the truth value of an atom to true/false and active updates set one atom to true/false and then compute derived updates using a logic program. Just like the atomic actions of dynamic logic, the atomic updates can be combined into update programs with the operators sequential composition, choice and iteration. We have one more update action (also present in dynamic logic): the test of a formula. •The declarative semantics for formulas and updates, is based on Kripke structures. Because of the specific language of updates, we have many properties of the semantics that are not present in propositional dynamic logic (PDL). One of these properties is that we can identify worlds with the same valuation in a structure, the resulting structure is called normalized, and it is equivalent to the original structure. •We have a proof system for PDDL which is soundand complete for the class of ‘full’ structures. For non-full structures, the main problem is the axiomatization of successor worlds. For the most interesting class of (non-full) structures given some set of constraints, we give a reduction of the non-full case to the full case. •We have a Plotkin-style operational semantics for update programs. The operational semantics allows us to compute the result of executing an update in some database state. A database state is a set of (propositional) formulas and specifies some subset of the worlds in a structure (the worlds in whichs the database state is true). The operational and declarative semantics are shown to be equivalent.

Transition systems as proposed by Hennessy & Plotkin are defined for a series of three languages featuring concurrency.The first has shuffle and local nondeterminancy, the second synchronization merge and local nondeterminacy, and the third synchronization merge and global nondeterminacy.The languages are all uniform in the sense that the elementary actions are uninterpreted.Throughout, infinite behaviour is taken into account and modelled with infinitary languages in the sense of Nivat.A comparison with denotational semantics is provided.For the first two languages, a linear time model suffices; for the third language a braching time model with processes in the sense of De Bakker & Zucker is described.In the comparison an important role is played by an intermediate semantics in the style of Hoare & Olderog's specification oriented semantics.A variant on the notion of ready set is employed here.Precise statements are given relating the various semantics in terms of a number of abstraction operators.