Chiu, Yiu Man Edmund. / Thesis submitted in: December 1999. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 269-248). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Beginning Story --- p.2 / Chapter 1.2 --- Background --- p.3 / Chapter 1.2.1 --- History of Nonmonotonic Reasoning --- p.3 / Chapter 1.2.2 --- Formalizations of Nonmonotonic Reasoning --- p.6 / Chapter 1.2.3 --- Belief Revision --- p.13 / Chapter 1.2.4 --- Network Representation of Knowledge --- p.17 / Chapter 1.2.5 --- Reference from Logic Programming --- p.21 / Chapter 1.2.6 --- Recent Work on Network-type Automatic Reasoning Sys- tems --- p.22 / Chapter 1.3 --- A Novel Inference Network Approach --- p.23 / Chapter 1.4 --- Objectives --- p.23 / Chapter 1.5 --- Organization of the Thesis --- p.24 / Chapter 2 --- The Predicate Inference Network PIN --- p.25 / Chapter 2.1 --- Preliminary Terms --- p.26 / Chapter 2.2 --- Overall Structure --- p.27 / Chapter 2.3 --- Object Layer --- p.29 / Chapter 2.3.1 --- Virtual Object --- p.31 / Chapter 2.4 --- Predicate Layer --- p.33 / Chapter 2.4.1 --- Node Values --- p.34 / Chapter 2.4.2 --- Information Source --- p.35 / Chapter 2.4.3 --- Belief State --- p.36 / Chapter 2.4.4 --- Predicates --- p.37 / Chapter 2.4.5 --- Prototypical Predicates --- p.37 / Chapter 2.4.6 --- Multiple Inputs for a Single Belief --- p.39 / Chapter 2.4.7 --- External Program Call --- p.39 / Chapter 2.5 --- Variable Layer --- p.40 / Chapter 2.6 --- Inter-Layer Links --- p.42 / Chapter 2.7 --- Chapter Summary --- p.43 / Chapter 3 --- Computation for PIN --- p.44 / Chapter 3.1 --- Computation Functions for Propagation --- p.45 / Chapter 3.1.1 --- Computational Functions for Combinative Links --- p.45 / Chapter 3.1.2 --- Computational Functions for Alternative Links --- p.49 / Chapter 3.2 --- Applying the Computation Functions --- p.52 / Chapter 3.3 --- Relations Represented in PIN --- p.55 / Chapter 3.3.1 --- Relations Represented by Combinative Links --- p.56 / Chapter 3.3.2 --- Relations Represented by Alternative Links --- p.59 / Chapter 3.4 --- Chapter Summary --- p.61 / Chapter 4 --- Dynamic Knowledge Update --- p.62 / Chapter 4.1 --- Operations for Knowledge Update --- p.63 / Chapter 4.2 --- Logical Expression --- p.63 / Chapter 4.3 --- Applicability of Operators --- p.64 / Chapter 4.4 --- Add Operation --- p.65 / Chapter 4.4.1 --- Add a fully instantiated single predicate proposition with no virtual object --- p.66 / Chapter 4.4.2 --- Add a fully instantiated pure disjunction --- p.68 / Chapter 4.4.3 --- Add a fully instantiated expression which is a conjunction --- p.71 / Chapter 4.4.4 --- Add a human biased relation --- p.74 / Chapter 4.4.5 --- Add a single predicate expression with virtual objects --- p.76 / Chapter 4.4.6 --- Add a IF-THEN rule --- p.80 / Chapter 4.5 --- Remove Operation --- p.88 / Chapter 4.5.1 --- Remove a Belief --- p.88 / Chapter 4.5.2 --- Remove a Rule --- p.91 / Chapter 4.6 --- Revise Operation --- p.94 / Chapter 4.6.1 --- Revise a Belief --- p.94 / Chapter 4.6.2 --- Revise a Rule --- p.96 / Chapter 4.7 --- Consistency Maintenance --- p.97 / Chapter 4.7.1 --- Logical Suppression --- p.98 / Chapter 4.7.2 --- Example on Handling Inconsistent Information --- p.99 / Chapter 4.8 --- Chapter Summary --- p.102 / Chapter 5 --- Knowledge Query --- p.103 / Chapter 5.1 --- Domains of Quantification --- p.104 / Chapter 5.2 --- Reasoning through Recursive Rules --- p.109 / Chapter 5.2.1 --- Infinite Looping Control --- p.110 / Chapter 5.2.2 --- Proof of the finite termination of recursive rules --- p.111 / Chapter 5.3 --- Query Functions --- p.117 / Chapter 5.4 --- Type I Queries --- p.119 / Chapter 5.4.1 --- Querying a Simple Single Predicate Proposition (Type I) --- p.122 / Chapter 5.4.2 --- Querying a Belief with Logical Connective(s) (Type I) --- p.128 / Chapter 5.5 --- Type II Queries --- p.132 / Chapter 5.5.1 --- Querying Single Predicate Expressions (Type II) --- p.134 / Chapter 5.5.2 --- Querying an Expression with Logical Connectives (Type II) --- p.143 / Chapter 5.6 --- Querying an Expression with Virtual Objects --- p.152 / Chapter 5.6.1 --- Type I Queries Involving Virtual Object --- p.152 / Chapter 5.6.2 --- Type II Queries involving Virtual Objects --- p.156 / Chapter 5.7 --- Chapter Summary --- p.157 / Chapter 6 --- Uniqueness and Finite Termination --- p.159 / Chapter 6.1 --- Proof Structure --- p.160 / Chapter 6.2 --- Proof for Completeness and Finite Termination of Domain Search- ing Procedure --- p.161 / Chapter 6.3 --- Proofs for Type I Queries --- p.167 / Chapter 6.3.1 --- Proof for Single Predicate Expressions --- p.167 / Chapter 6.3.2 --- Proof of Type I Queries on Expressions with Logical Con- nectives --- p.172 / Chapter 6.3.3 --- General Proof for Type I Queries --- p.174 / Chapter 6.4 --- Proofs for Type II Queries --- p.175 / Chapter 6.4.1 --- Proof for Type II Queries on Single Predicate Expressions --- p.176 / Chapter 6.4.2 --- Proof for Type II Queries on Disjunctions --- p.178 / Chapter 6.4.3 --- Proof for Type II Queries on Conjunctions --- p.179 / Chapter 6.4.4 --- General Proof for Type II Queries --- p.181 / Chapter 6.5 --- Proof for Queries Involving Virtual Objects --- p.182 / Chapter 6.6 --- Uniqueness and Finite Termination of PIN Queries --- p.183 / Chapter 6.7 --- Chapter Summary --- p.184 / Chapter 7 --- Lifschitz's Benchmark Problems --- p.185 / Chapter 7.1 --- Structure --- p.186 / Chapter 7.2 --- Default Reasoning --- p.186 / Chapter 7.2.1 --- Basic Default Reasoning --- p.186 / Chapter 7.2.2 --- Default Reasoning with Irrelevant Information --- p.187 / Chapter 7.2.3 --- Default Reasoning with Several Defaults --- p.188 / Chapter 7.2.4 --- Default Reasoning with a Disabled Default --- p.190 / Chapter 7.2.5 --- Default Reasoning in Open Domain --- p.191 / Chapter 7.2.6 --- Reasoning about Unknown Exceptions I --- p.193 / Chapter 7.2.7 --- Reasoning about Unknown Exceptions II --- p.194 / Chapter 7.2.8 --- Reasoning about Unknown Exceptions III --- p.196 / Chapter 7.2.9 --- Priorities between Defaults --- p.198 / Chapter 7.2.10 --- Priorities between Instances of a Default --- p.199 / Chapter 7.2.11 --- Reasoning about Priorities --- p.199 / Chapter 7.3 --- Inheritance --- p.200 / Chapter 7.3.1 --- Linear Inheritance --- p.200 / Chapter 7.3.2 --- Tree-Structured Inheritance --- p.202 / Chapter 7.3.3 --- One-Step Multiple Inheritance --- p.203 / Chapter 7.3.4 --- Multiple Inheritance --- p.204 / Chapter 7.4 --- Uniqueness of Names --- p.205 / Chapter 7.4.1 --- Unique Names Hypothesis for Objects --- p.205 / Chapter 7.4.2 --- Unique Names Hypothesis for Functions --- p.206 / Chapter 7.5 --- Reasoning about Action --- p.206 / Chapter 7.6 --- Autoepistemic Reasoning --- p.206 / Chapter 7.6.1 --- Basic Autoepistemic Reasoning --- p.206 / Chapter 7.6.2 --- Autoepistemic Reasoning with Incomplete Information --- p.207 / Chapter 7.6.3 --- Autoepistemic Reasoning with Open Domain --- p.207 / Chapter 7.6.4 --- Autoepistemic Default Reasoning --- p.208 / Chapter 8 --- Comparison with PROLOG --- p.214 / Chapter 8.1 --- Introduction of PROLOG --- p.215 / Chapter 8.1.1 --- Brief History --- p.215 / Chapter 8.1.2 --- Structure and Inference --- p.215 / Chapter 8.1.3 --- Why Compare PIN with Prolog --- p.216 / Chapter 8.2 --- Representation Power --- p.216 / Chapter 8.2.1 --- Close World Assumption and Negation as Failure --- p.216 / Chapter 8.2.2 --- Horn Clauses --- p.217 / Chapter 8.2.3 --- Quantification --- p.218 / Chapter 8.2.4 --- Build-in Functions --- p.219 / Chapter 8.2.5 --- Other Representation Issues --- p.220 / Chapter 8.3 --- Inference and Query Processing --- p.220 / Chapter 8.3.1 --- Unification --- p.221 / Chapter 8.3.2 --- Resolution --- p.222 / Chapter 8.3.3 --- Computation Efficiency --- p.225 / Chapter 8.4 --- Knowledge Updating and Consistency Issues --- p.227 / Chapter 8.4.1 --- PIN and AGM Logic --- p.228 / Chapter 8.4.2 --- Knowledge Merging --- p.229 / Chapter 8.5 --- Chapter Summary --- p.229 / Chapter 9 --- Conclusion and Discussion --- p.230 / Chapter 9.1 --- Conclusion --- p.231 / Chapter 9.1.1 --- General Structure --- p.231 / Chapter 9.1.2 --- Representation Power --- p.231 / Chapter 9.1.3 --- Inference --- p.232 / Chapter 9.1.4 --- Dynamic Update and Consistency --- p.233 / Chapter 9.1.5 --- Soundness and Completeness Versus Efficiency --- p.233 / Chapter 9.2 --- Discussion --- p.234 / Chapter 9.2.1 --- Different Selection Criteria --- p.234 / Chapter 9.2.2 --- Link Order --- p.235 / Chapter 9.2.3 --- Inheritance Reasoning --- p.236 / Chapter 9.3 --- Future Work --- p.237 / Chapter 9.3.1 --- Implementation --- p.237 / Chapter 9.3.2 --- Application --- p.237 / Chapter 9.3.3 --- Probabilistic and Fuzzy PIN --- p.238 / Chapter 9.3.4 --- Temporal Reasoning --- p.238 / Bibliography --- p.239

The goal of this thesis is twofold: first, we want to present the distributed belief accommodation and revision model for multi-agent systems that has been developed and, second, we wish to show its applicability to an appropriate domain. The Distributed Belief Accommodation & Revision model, called DeBAteR model, was developed for co-operative heterogeneous multi-agent systems used to model inherently dynamic distributed problems. In these systems, although the agents are able to detect changes both in the environment and in the problem specifications, each agent has only a partial view of the global picture. As a result the information that represents the current state of affairs is dynamic, incomplete and sometimes uncertain. This non-monotonic kind of data is called beliefs ? a belief is a piece of data that is held as correct as long as no contradicting evidence is found or presented. Each agent is expected to include an assumption based truth maintenance module for representing properly this type of data. Our main effort was concentrated on the task of maintaining the system's information, which consists of updating, revising and accommodating the represented beliefs. Belief updating is necessary for including the changes detected by the agents both in the environment and/or in the problem specifications. Belief revision is essential for solving the inconsistencies detected among the represented beliefs. Belief accommodation and revision is crucial for integrating the multiple disparate perspectives regarding the same data items, which may occur whenever there is overlap of expertise domains between the agents. In order to solve the information conflicts that result from the detection of inconsistencies between distinct beliefs or within multi-perspective beliefs we conceived the DeBATeR model. The DeBAteR is fully distributed, provides individual belief autonomy and is made of two methodologies: the pro-active belief accommodation and revision methodology and the delayed belief revision methodology. Whilst the first methodology is used to solve domain independent conflicts, the second methodology was devised for solving domain dependent conflicts. Both methodologies use argumentation for, in the case of the domain independent conflicts, choosing the most credible perspective between the existing multiple perspectives of a belief, and, in the case of the domain dependent conflicts, finding the best alternative belief support set for the affected concepts. These methodologies are distributed and their scope may be internal or collective. The DeBAteR model main contributions are: (i) the pro-active methodology conceived for solving domain independent conflicts and (ii) the capability, not only to represent and maintain individual beliefs and joint beliefs, but also to accommodate, rationally maintain and make use of multi-perspective beliefs. Finally, we describe the developed decision support multi-agent system for choosing adequate project locations, called DIPLOMAT ? Dynamic and Interactive Project Location Test bed, which has the ability of accommodating and revising the represented beliefs according to the DeBAteR model methodologies. / O objectivo desta dissertação é duplo: por um lado, pretendemos dar a conhecer o modelo de revisão e acomodação de crenças para sistemas multi-agente por nós desenvolvido e, por outro, procuramos ilustrar a sua validade descrevendo a aplicação que realizámos. O modelo, designado DeBAteR ? Distributed Belief Accommodation & Revision, destina-se a sistemas multi-agente cooperativos e heterogéneos que modelam problemas inerentemente distribuídos e dinâmicos. Neste tipo de sistemas, a informação que representa o ambiente é dinâmica (os agentes possuem a capacidade de constatar alterações no ambiente e/ou nas condições do problema) e, muitas vezes, incompleta (os agentes possuem visões parcelares da realidade) e/ou incerta. Este tipo de informação, de carácter não definitivo, designa-se por crenças ? uma crença é uma convicção tida como correcta enquanto não for posta em causa por alguma evidência. Cada agente, a fim de representar e manipular crenças, foi enriquecido com um módulo específico de manutenção de consistência baseado em suposições. O nosso esforço concentrou-se na tarefa de manutenção (actualização, revisão e acomodação) da informação do sistema. A actualização de crenças é essencial para incorporar as alterações que os agentes detectam no ambiente e/ou nas condições do problema. A revisão de crenças é indispensável para resolver inconsistências (conflitos) entre as crenças representadas. A acomodação e revisão simultânea de crenças é imprescindível para a integração das múltiplas perspectivas díspares que surgem em relação a um mesmo item de informação (crenças pluri-perspectiva) quando existe sobreposição de domínios de especialidade entre os agentes. Para tentar solucionar estes conflitos entre crenças concebemos um modelo distribuído que assegura autonomia individual de crença. O modelo de acomodação e revisão de crenças DeBAteR é composto por duas metodologias: a metodologia pró-activa de acomodação e revisão de crenças pluri-perspectiva e a metodologia retardada de revisão de crenças. A primeira, destina-se a resolver conflitos independentes do domínio e a segunda destina-se à resolução de conflitos dependentes do domínio. Estas metodologias são suportadas quase integralmente por um sistema de argumentação que procura, no caso dos conflitos independentes do domínio, escolher a perspectiva mais credível e, no caso dos conflitos dependentes do domínio, encontrar o melhor conjunto alternativo de suporte para os conceitos afectados. Esta actividade é descentralizada e pode decorrer quer no âmbito intra-agente, quer no âmbito inter-agente. É ainda de realçar: (i) o carácter pró-activo da resolução dos conflitos independentes do domínio (crenças pluri-perspectiva) e (ii) a capacidade de, não só, representar e manter crenças de âmbito individual (crenças suportadas por apenas um agente) e crenças conjuntas (crenças suportadas por vários agentes), mas também, de sintetizar, manter racionalmente e utilizar crenças pluri-perspectiva. Por último, descrevemos o sistema multi-agente de apoio à decisão no domínio da localização de empreendimentos desenvolvido, denominado DIPLOMAT ? Dynamic and Interactive Project Location Test bed, o qual possui a capacidade de acomodar e rever crenças de acordo com as metodologias concebidas no âmbito do modelo DeBAteR.

We provide a systematic in-depth study of the semantics of abstract dialectical frameworks (ADFs), a recent generalisation of Dung\'s abstract argumentation frameworks. This is done by associating with an ADF its characteristic one-step consequence operator and defining various semantics for ADFs as different fixpoints of this operator. We first show that several existing semantical notions are faithfully captured by our definition, then proceed to define new ADF semantics and show that they are proper generalisations of existing argumentation semantics from the literature. Most remarkably, this operator-based approach allows us to compare ADFs to related nonmonotonic formalisms like Dung argumentation frameworks and propositional logic programs. We use polynomial, faithful and modular translations to relate the formalisms, and our results show that both abstract argumentation frameworks and abstract dialectical frameworks are at most as expressive as propositional normal logic programs.

abstract: Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former. / Dissertation/Thesis / Ph.D. Computer Science 2012

VERAS, Rodrigo de Melo Souza. Uma nova abordagem para o raciocinio não monotônico. 2007. 102 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2007. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-12T16:47:33Z No. of bitstreams: 1 2007_dis_rmsveras.pdf: 1095797 bytes, checksum: 0e461d552478b8849c4691529b7a5a91 (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-21T16:12:24Z (GMT) No. of bitstreams: 1 2007_dis_rmsveras.pdf: 1095797 bytes, checksum: 0e461d552478b8849c4691529b7a5a91 (MD5) / Made available in DSpace on 2016-07-21T16:12:24Z (GMT). No. of bitstreams: 1 2007_dis_rmsveras.pdf: 1095797 bytes, checksum: 0e461d552478b8849c4691529b7a5a91 (MD5) Previous issue date: 2007 / Default logic was introduced to manipulate reasoning with incomplete information and became the main paradigm to formalize nonmonotonic reasoning. Many variations have been proposed with the objective to solve some limitations of the formalism or to consider different intuitions on the role of inconclusive information. However, some of the main characteristics had been kept: inconclusive information is represented as default rules, objectifies the calculation of extensions and, because this, they use characterizations through fixed-point operators. We consider a new approach for nonmonotonic reasoning. In this dissertation, we present the Defeasible Logic with Exception-First. The main advantages of this approach are: it does not use fixed-points operators to define expansions (our correspondent of extensions) and exception-first property that does not allow that a inconclusive proposition intervenes with the derivation of its exception. Moreover, we present a new way to define the extensions of default logic and two of its main variants, justified default logic and constrained default logic. / A lógica default foi introduzida para manipular raciocínio com conhecimento incompleto e tornou-se o principal paradigma para a formalização do raciocínio não monotônico. Muitas variações foram propostas com o objetivo de solucionar algumas limitações do formalismo ou para proporem diferentes intuições sobre o papel das informações inconlusivas. Porém, algumas das principais características foram mantidas: a informação inconclusiva é representada por regras default, objetivam o cálculo de extensões e, para isso, utilizam uma caracterização através de operadores de ponto fixo. Nós propomos uma nova abordagem para o raciocínio não monotônico. Nesta dissertação, apresentamos a Lógica Defeasible com Prioridade às Exceções. As principais vantagens desta proposta são a não utilização de pontos fixos para definição das expansões (nosso correspondente de extensões) e a propriedade de prioridade às exceções que não permite que uma proposição inconclusiva interfira na derivação de sua exceção. Além disso, apresentamos uma nova maneira de definir as extensões da lógica default e de duas das suas principais variantes a lógica default justificada e a lógica default com restrições.

nÃo hà / A lÃgica default foi introduzida para manipular raciocÃnio com conhecimento incompleto e tornou-se o principal paradigma para a formalizaÃÃo do raciocÃnio nÃo monotÃnico. Muitas variaÃÃes foram propostas com o objetivo de solucionar algumas limitaÃÃes do formalismo ou para proporem diferentes intuiÃÃes sobre o papel das informaÃÃes inconlusivas. PorÃm, algumas das principais caracterÃsticas foram mantidas: a informaÃÃo inconclusiva à representada por regras default, objetivam o cÃlculo de extensÃes e, para isso, utilizam uma caracterizaÃÃo atravÃs de operadores de ponto fixo. NÃs propomos uma nova abordagem para o raciocÃnio nÃo monotÃnico. Nesta dissertaÃÃo, apresentamos a LÃgica Defeasible com Prioridade Ãs ExceÃÃes. As principais vantagens desta proposta sÃo a nÃo utilizaÃÃo de pontos fixos para definiÃÃo das expansÃes (nosso correspondente de extensÃes) e a propriedade de prioridade Ãs exceÃÃes que nÃo permite que uma proposiÃÃo inconclusiva interfira na derivaÃÃo de sua exceÃÃo. AlÃm disso, apresentamos uma nova maneira de definir as extensÃes da lÃgica default e de duas das suas principais variantes a lÃgica default justificada e a lÃgica default com restriÃÃes. / Default logic was introduced to manipulate reasoning with incomplete information and became the main paradigm to formalize nonmonotonic reasoning. Many variations have been proposed with the objective to solve some limitations of the formalism or to consider different intuitions on the role of inconclusive information. However, some of the main characteristics had been kept: inconclusive information is represented as default rules, objectifies the calculation of extensions and, because this, they use characterizations through fixed-point operators. We consider a new approach for nonmonotonic reasoning. In this dissertation, we present the Defeasible Logic with Exception-First. The main advantages of this approach are: it does not use fixed-points operators to define expansions (our correspondent of extensions) and exception-first property that does not allow that a inconclusive proposition intervenes with the derivation of its exception. Moreover, we present a new way to define the extensions of default logic and two of its main variants, justified default logic and constrained default logic.

The field of artificial intelligence, research on knowledge representation and reasoning has originated a large variety of formats, languages, and formalisms. Over the decades many different tools emerged to use these underlying concepts. Each one has been designed with some specific application in mind and are even used nowadays, where the internet is seen as a service to be sufficient for the age of Industry 4.0 and the Internet of Things. In that vision of a connected world, with these many different formalisms and systems, a formal way to uniformly exchange information, such as knowledge and belief is imperative. That alone is not enough, because even more systems get integrated into the online world and nowadays we are confronted with a huge amount of continuously flowing data. Therefore a solution is needed to both, allowing the integration of information and dynamic reaction to the data which is provided in such continuous data-flow environments. This work aims to present a unique and novel pair of formalisms to tackle these two important needs by proposing an abstract and general solution. We introduce and discuss reactive Multi-Context Systems (rMCS), which allow one to utilise different knowledge representation formalisms, so-called contexts which are represented as an abstract logic framework, and exchange their beliefs through bridge rules with other contexts. These multiple contexts need to mutually agree on a common set of beliefs, an equilibrium of belief sets. While different Multi-Context Systems already exist, they are only solving this agreement problem once and are neither considering external data streams, nor are they reasoning continuously over time. rMCS will do this by adding means of reacting to input streams and allowing the bridge rules to reason with this new information. In addition we propose two different kind of bridge rules, declarative ones to find a mutual agreement and operational ones for adapting the current knowledge for future computations. The second framework is more abstract and allows computations to happen in an asynchronous way. These asynchronous Multi-Context Systems are aimed at modelling and describing communication between contexts, with different levels of self-management and centralised management of communication and computation. In this thesis rMCS will be analysed with respect to usability, consistency management, and computational complexity, while we will show how asynchronous Multi-Context Systems can be used to capture the asynchronous ideas and how to model an rMCS with it. Finally we will show how rMCSs are positioned in the current world of stream reasoning and that it can capture currently used technologies and therefore allows one to seamlessly connect different systems of these kinds with each other. Further on this also shows that rMCSs are expressive enough to simulate the mechanics used by these systems to compute the corresponding results on its own as an alternative to already existing ones. For asynchronous Multi-Context Systems, we will discuss how to use them and that they are a very versatile tool to describe communication and asynchronous computation.

We provide a systematic in-depth study of the semantics of abstract dialectical frameworks (ADFs), a recent generalisation of Dung\''s abstract argumentation frameworks. This is done by associating with an ADF its characteristic one-step consequence operator and defining various semantics for ADFs as different fixpoints of this operator. We first show that several existing semantical notions are faithfully captured by our definition, then proceed to define new ADF semantics and show that they are proper generalisations of existing argumentation semantics from the literature. Most remarkably, this operator-based approach allows us to compare ADFs to related nonmonotonic formalisms like Dung argumentation frameworks and propositional logic programs. We use polynomial, faithful and modular translations to relate the formalisms, and our results show that both abstract argumentation frameworks and abstract dialectical frameworks are at most as expressive as propositional normal logic programs.

The ability to change one's beliefs in a rational manner is one of many facets of the abilities of an intelligent agent. Central to any investigation of belief change is the notion of an epistemic state. This dissertation is mainly concerned with three issues involving epistemic states: 1. How should an epistemic state be represented? 2. How does an agent use an epistemic state to perform belief change? 3. How does an agent arrive at a particular epistemic state? With regard to the first question, note that there are many different methods for constructing belief change operations. We argue that semantic constructions involving ordered pairs, each consisting of a set of beliefs and an ordering on the set of "possible worlds" (or equivalently, on the set of basic independent bits of information) are, in an important sense, more fundamental. Our answer to the second question provides indirect support for the use of semantic structures. We show how well-known belief change operations and related structures can be modelled semantically. Furthermore, we introduce new forms of belief change related operations and structures which are all defined, and motivated, in terms of such semantic representational formalisms. These include a framework for unifying belief revision and nonmonotonic reasoning, new versions of entrenchment orderings on beliefs, novel approaches to withdrawal operations, and an expanded view of iterated belief change. The third question is. one which has not received much attention in the belief change literature. We propose to extract extra-logical information from the formal representation of an agent's set of beliefs, which can then be used in the construction of epistemic state. his proposal is just a first approximation, although it seems to have the potential for developing into a full-fledged theory. / Computing / D.Phil.(Computer Science)

The ability to change one's beliefs in a rational manner is one of many facets of the abilities of an intelligent agent. Central to any investigation of belief change is the notion of an epistemic state. This dissertation is mainly concerned with three issues involving epistemic states: 1. How should an epistemic state be represented? 2. How does an agent use an epistemic state to perform belief change? 3. How does an agent arrive at a particular epistemic state? With regard to the first question, note that there are many different methods for constructing belief change operations. We argue that semantic constructions involving ordered pairs, each consisting of a set of beliefs and an ordering on the set of "possible worlds" (or equivalently, on the set of basic independent bits of information) are, in an important sense, more fundamental. Our answer to the second question provides indirect support for the use of semantic structures. We show how well-known belief change operations and related structures can be modelled semantically. Furthermore, we introduce new forms of belief change related operations and structures which are all defined, and motivated, in terms of such semantic representational formalisms. These include a framework for unifying belief revision and nonmonotonic reasoning, new versions of entrenchment orderings on beliefs, novel approaches to withdrawal operations, and an expanded view of iterated belief change. The third question is. one which has not received much attention in the belief change literature. We propose to extract extra-logical information from the formal representation of an agent's set of beliefs, which can then be used in the construction of epistemic state. his proposal is just a first approximation, although it seems to have the potential for developing into a full-fledged theory. / Computing / D.Phil.(Computer Science)