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28
Enhancing Model Checking in Verification by AI Techniques
 Artificial Intelligence
, 1999
"... Model checking is a fruitful application of computational logic with high relevance to the verification of concurrent systems. While model checking is capable of automatically testing that a concurrent system satisfies its formal specification, it can not precisely locate an error and suggest a r ..."
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Model checking is a fruitful application of computational logic with high relevance to the verification of concurrent systems. While model checking is capable of automatically testing that a concurrent system satisfies its formal specification, it can not precisely locate an error and suggest a repair, i.e., a suitable correction, to the system. In this paper, we tackle this problem by using principles from AI. In particular, we introduce the abstract concept of a system repair problem, and exemplify this concept on repair of concurrent programs and protocols. For the development of our framework, we formally extend the concept of counterexample, which has been proposed in model checking previously, and provide examples which demonstrate the need for such an extension. Moreover, we investigate into optimization issues for the problem of finding a repair, and present techniques which gain in some cases a considerable reduction of the search space for a repair.
What makes propositional abduction tractable
 Artificial Intelligence
"... Abduction is a fundamental form of nonmonotonic reasoning that aims at finding explanations for observed manifestations. This process underlies many applications, from car configuration to medical diagnosis. We study here the computational complexity of deciding whether an explanation exists in the ..."
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Abduction is a fundamental form of nonmonotonic reasoning that aims at finding explanations for observed manifestations. This process underlies many applications, from car configuration to medical diagnosis. We study here the computational complexity of deciding whether an explanation exists in the case when the application domain is described by a propositional knowledge base. Building on previous results, we classify the complexity for local restrictions on the knowledge base and under various restrictions on hypotheses and manifestations. In comparison to the many previous studies on the complexity of abduction we are able to give a much more detailed picture for the complexity of the basic problem of deciding the existence of an explanation. It turns out that depending on the restrictions, the problem in this framework is always polynomialtime solvable, NPcomplete, coNPcomplete, or ΣP2complete. Based on these results, we give an a posteriori justification of what makes propositional abduction hard even for some classes of knowledge bases which allow for efficient satisfiability testing and deduction. This justification is very simple and intuitive, but it reveals that no nontrivial class of abduction problems is tractable. Indeed, tractability essentially requires that the language for knowledge bases is unable to express both causal links and conflicts between hypotheses. This generalizes a similar observation by Bylander et al. for setcovering abduction.
Abductive reasoning through Filtering
 Artificial Intelligence
, 2000
"... Abduction is an inference mechanism where given a knowledge base and some observations, the reasoner tries to find hypotheses which together with the knowledge base explain the observations. A reasoning based on such an inference mechanism is referred to as abductive reasoning. Given a theory and so ..."
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Abduction is an inference mechanism where given a knowledge base and some observations, the reasoner tries to find hypotheses which together with the knowledge base explain the observations. A reasoning based on such an inference mechanism is referred to as abductive reasoning. Given a theory and some observations, by filtering the theory with the observations, we mean selecting only those models of the theory that entail the observations. Entailment with respect to these selected models is referred to as filter entailment. In this paper we give necessary and sufficient conditions when abductive reasoning with respect to a theory and some observations is equivalent to the corresponding filter entailment. We then give sufficiency conditions for particular knowledge representation formalisms that guarantee that abductive reasoning can indeed be done through filtering and present examples from the knowledge representation literature where abductive reasoning is done through filtering. We...
Real arguments are approximate arguments
 In AAAI’07, 66–71
"... There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logicbased argumentation is that an argument is a pair 〈Φ, α 〉 where Φ is minimal su ..."
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Cited by 9 (4 self)
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There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logicbased argumentation is that an argument is a pair 〈Φ, α 〉 where Φ is minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. However, real arguments (i.e. arguments presented by humans) usually do not have enough explicitly presented premises for the entailment of the claim. This is because there is some common knowledge that can be assumed by a proponent of an argument and the recipient of it. This allows the proponent of an argument to encode an argument into a real argument by ignoring the common knowledge, and it allows a recipient of a real argument to decode it into an argument by drawing on the common knowledge. If both the proponent and recipient use the same common knowledge, then this process is straightforward. Unfortunately, this is not always the case, and raises the need for an approximation of the notion of an argument for the recipient to cope with the disparities between the different views on what constitutes common knowledge.
Using Enthymemes in an Inquiry Dialogue System
"... A common assumption for logicbased argumentation is that an argument is a pair 〈Φ, α 〉 where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. However, real arguments (i.e. arguments presented by humans) usually do not have enough explicitly presented p ..."
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Cited by 8 (2 self)
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A common assumption for logicbased argumentation is that an argument is a pair 〈Φ, α 〉 where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. However, real arguments (i.e. arguments presented by humans) usually do not have enough explicitly presented premises for the entailment of the claim (i.e. they are enthymemes). This is because there is some common knowledge that can be assumed by a proponent of an argument and the recipient of it. This allows the proponent of an argument to encode an argument into a real argument by ignoring the common knowledge, and it allows a recipient of a real argument to decode it into the intended argument by drawing on the common knowledge. If both the proponent and recipient use the same common knowledge, then this process is straightforward. Unfortunately, this is not always the case, and this raises interesting issues for dialogue systems in which the recipient has to cope with the disparities between the different views on what constitutes common knowledge. Here we investigate the use of enthymemes in inquiry dialogues. For this, we propose a generative inquiry dialogue system and show how, in this dialogue system, enthymemes can be managed by the agents involved, and how common knowledge can evolve through dialogue.
Ordered Diagnosis
, 2003
"... We propose to regard a diagnostic system as an ordered logic theory, i.e. a partially ordered set of clauses where smaller rules carry more preference. ..."
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Cited by 7 (6 self)
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We propose to regard a diagnostic system as an ordered logic theory, i.e. a partially ordered set of clauses where smaller rules carry more preference.
Ordered Programs as Abductive Systems
 In Proceedings of the APPIAGULPPRODE Conference on Declarative Programming (AGP2003
, 2003
"... In ordered logic programs, i.e. partially ordered sets of clauses where smaller rules carry more preference, inconsistencies, which appear as conflicts between applicable rules, are handled by satisfying more preferred rules, at the expense of defeating lesser rules. We show that this formalism c ..."
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Cited by 5 (5 self)
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In ordered logic programs, i.e. partially ordered sets of clauses where smaller rules carry more preference, inconsistencies, which appear as conflicts between applicable rules, are handled by satisfying more preferred rules, at the expense of defeating lesser rules. We show that this formalism can be exploited to obtain a simple implementation of abductive systems, where abducibles are assumed false by default, but weaker rules can be used to introduce them, if necessary. Moreover, the approach can be extended, without leaving the ordered programming framework, to support abductive systems involving preference, either on the set of abducibles or on the system description. The latter case appears naturally in applications such as legal reasoning where rules carry a natural precedence. However, combining preference on abducibles with a complex theory structure brings the complexity, e.g. of the relevance problem, to 3 , and thus such systems cannot be simulated by ordered programs.
Outlier Detection Using Default Logic
 In Proc. of the 18th International Joint Conference on Artificial Intelligence (IJCAI
, 2003
"... Default logic is used to describe regular behavior and normal properties. We suggest to exploit the framework of default logic for detecting outliers  individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledgebase integ ..."
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Cited by 5 (3 self)
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Default logic is used to describe regular behavior and normal properties. We suggest to exploit the framework of default logic for detecting outliers  individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledgebase integrity and to single out irregular individuals. We first formally define the notion of an outlier and an outlier witness. We then show that finding outliers is quite complex. Indeed, we show that several versions of the outlier detection problem lie over the second level of the polynomial hierarchy. For example, the question of establishing if at least one outlier can be detected in a given propositional default theory is complete. Although outlier detection involves heavy computation, the queries involved can frequently be executed offline, thus somewhat alleviating the difficulty of the problem. In addition, we show that outlier detection can be done in polynomial time for both the class of acyclic normal unary defaults and the class of acyclic dual normal unary defaults. 1
Expressing Default Abduction Problems as Quantified Boolean Formulas
 AI Communications
, 2002
"... Abduction is the process of finding explanations for observed phenomena in accord to known laws about a given application domain. This form of reasoning is an important principle of commonsense reasoning and is particularly relevant in conjunction with nonmonotonic knowledge representation formalis ..."
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Cited by 4 (3 self)
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Abduction is the process of finding explanations for observed phenomena in accord to known laws about a given application domain. This form of reasoning is an important principle of commonsense reasoning and is particularly relevant in conjunction with nonmonotonic knowledge representation formalisms. In this paper, we deal with a model for abduction in which the domain knowledge is represented in terms of a default theory. We show how the main reasoning tasks associated with this particular form of abduction can be axiomatised within the language of quantified Boolean logic. More specifically, we provide polynomialtime constructible reductions mapping a given abduction problem into a quantified Boolean formula (QBF) such that the satisfying truth assignments to the free variables of the latter determine the solutions of the original problem. Since there are now efficient QBFsolvers available, this reduction technique yields a straightforward method to implement the discussed abduction tasks. We describe a realisation of this approach by appeal to the reasoning system QUIP.