Forgetting is removing variables from a logical formula while preserving the constraints on the other variables. In spite of reducing information, it does not always decrease the size of the formula and may sometimes increase it. This article discusses the implications of such an increase and analyzes the computational properties of the phenomenon. Given a propositional Horn formula, a set of variables and a maximum allowed size, deciding whether forgetting the variables from the formula can be expressed in that size is Dp-hard in Σ2p. The same problem for unrestricted CNF propositional formulae is D2p-hard in Σ3p.

Abstract Logical forgetting is removing some variables from a formula while maintaining its consequences on the remaining variables. This removal may require exponential time on arbitrary propositional Horn formulae, but it only takes polynomial time on single-head propositional definite Horn formulae. Single-head means that no variable is the head of multiple clauses. An algorithm to make a formula single-head if possible is shown. It enlarges the set of formulae admitting polynomial-time forgetting by making them single-head if possible and then running the usual algorithm for forgetting.

Several forms of iterable belief change exist, differing in the kind of change and its strength: some operators introduce formulae, others remove them; some add formulae unconditionally, others only as additions to the previous beliefs; some only relative to the current situation, others in all possible cases. A sequence of changes may involve several of them: for example, the first step is a revision, the second a contraction and the third a refinement of the previous beliefs. The ten operators considered in this article are shown to be all reducible to three: lexicographic revision, refinement, and severe withdrawal. In turn, these three can be expressed in terms of lexicographic revision at the cost of restructuring the sequence. This restructuring needs not to be done explicitly: an algorithm that works on the original sequence is shown. The complexity of mixed sequences of belief change operators is also analyzed. Most of them require only a polynomial number of calls to a satisfiability checker, some are even easier.

Merging beliefs requires the plausibility of the sources of the information to be merged. They are typically assumed equally reliable when nothing suggests otherwise. A recent line of research has spun from the idea of deriving this information from the revision process itself. In particular, the history of previous revisions and previous merging examples provide information for performing subsequent merging operations.Yet, no examples or previous revisions may be available. In spite of the apparent lack of information, something can still be inferred by a try-and-check approach: a relative reliability ordering is assumed, the sources are integrated according to it and the result is compared with the original information. The final check may contradict the original ordering, like when the result of merging implies the negation of a formula coming from a source initially assumed reliable, or it implies a formula coming from a source assumed unreliable. In such cases, the reliability ordering assumed in the first place can be excluded from consideration.Such a scenario is proved real under the classifications of source reliability and definitions of belief integration considered in this article: sources divided in two, three or multiple reliability classes; integration is mostly by maximal consistent subsets but also weighted distance is considered. Other results mainly concern the integration by maximal consistent subsets and partitions of two and three reliability classes.

Temporal Logic Model Checking is a verification method in which we describe a system, the model, and then we verify whether important properties, expressed in a temporal logic formula, hold in the system. Many Model Checking tools employ BDDs or some other data structure to represent sets of states. It has been empirically observed that the BDDs used in these algorithms may grow exponentially as the model and formula increase in size. We formally prove that no kind of data structure of polynomial size can represent the set of valid initial states for all models and all formulae. This result holds for all data structures where a state can be checked in polynomial time. Therefore, it holds not only for all types of BDDs regardless of variable ordering, but also for more powerful data structures, such as RBCs, MTBDDs, ADDs and SDDs. Thus, the size explosion of BDDs is not a limit of these specific data representation structures, but is unavoidable: every formalism used in the same way would lead to an exponential size blow up.

A common assumption in belief revision is that the reliability of the information sources is either given, derived from temporal information, or the same for all. This article does not describe a new semantics for integration but studies the problem of obtaining the reliability of the sources given the result of a previous merging. As an example, corrections performed manually on the result of merging some databases may indicate that the relative reliability of their sources is different from what was previously assumed, helping subsequent data mergings.

This article proposes a solution to the problem of obtaining plausibility information, which is necessary to perform belief revision: given a sequence of revisions, together with their results, derive a possible initial order that has generated them; this is different from the usual assumption of starting from an all-equal initial order and modifying it by a sequence of revisions. Four semantics for iterated revision are considered: natural, restrained, lexicographic and reinforcement. For each, a necessary and sufficient condition to the existence of an order generating a given history of revisions and results is proved. Complexity is proved coNP complete in all cases but one (reinforcement revision with unbounded sequence length).

In this article, we report results about translations between variants of defaults logics such that the extensions of the theories that are the input and the output of the translation are in a bijective correspondence. We assume that a translation can introduce new variables and that the result of translating a theory can either be produced in time polynomial in the size of the theory or its output is of size polynomial in the size of the theory; we restrict to the case in which the original theory has extensions. This study fills a gap between two previous works, one studying bijective translations among restrictions of default logics and one studying non-bijective translations between default variants of default logic.

Reiter's original definition of default logic allows for the application of a default that contradicts one previously applied. We call this condition failure . The possibility of generating failures has been in the past considered a semantical problem, and variants have been proposed to solve it. We show that it is instead a computational feature that is needed to encode some domains into default logic.

Abduction is one of the most important forms of reasoning; it has been successfully applied to several practical problems, such as diagnosis. In this article we investigate whether the computational complexity of abduction can be reduced by an appropriate use of preprocessing. This is motivated by the fact that part of the data of the problem (namely, the set of all possible assumptions and the theory relating assumptions and manifestations) is often known before the rest of the problem. In this article, we show some complexity results about abduction when compilation is allowed.

In this paper, we define and study the k-approximating circuits. A circuit accepting a given set of inputs A is k-approximated by accepting inputs that lifter from one of A by at most k bits. We show that the existence of polynomial-size k-approximating circuits depends on the relation between k and the number of inputs.

DPLL and resolution are two popular methods for solving the problem of propositional satisfiability. Rather than algorithms, they are families of algorithms, as their behavior depends on some choices they face during execution: DPLL depends on the choice of the literal to branch on; resolution depends on the choice of the pair of clauses to resolve at each step. The complexity of making the optimal choice is analyzed in this article. Extending previous results, we prove that choosing the optimal literal to branch on in DPLL is Δ p 2 [log n ]-hard, and becomes NP PP -hard if branching is only allowed on a subset of variables. Optimal choice in regular resolution is both NP-hard and coNP-hard. The problem of determining the size of the optimal proofs is also analyzed: it is coNP-hard for DPLL, and Δ p 2 [log n ]-hard if a conjecture we make is true. This problem is coNP-hard for regular resolution.

This article reports complexity results on diagnosis of systems modeled as graphs. In this model introduced by Rao and Viswanadham, each component is a node of a graph, and an edge indicates that faults propagate from one component to the other one. The basic problem of diagnosis is known to be polynomial and NP-complete in the cases of single faults and multiple faults, respectively. We extend the complexity analysis to the case of faulty alarms, and also consider the problem of limiting the propagation of faults. We show that none of the considered diagnosis problems can be simplified by preprocessing the graph. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1053–1076, 2005.

This paper analyses the computational complexity of problems related to case-based planning: planning when a plan for a similar instance is known, and planning from a library of plans. It is proven that planning from a single case has the same complexity than generative planning (i.e. planning ‘from scratch’); using an extended definition of cases, complexity is reduced if the domain stored in the case is similar to the one to search plans for. Planning from a library of cases is shown to have the same complexity. In both cases, the complexity of planning remains, in the worst case, PSPACE-complete.

Policies of Markov Decision Processes (MDPs) determine the next action to execute from the current state and, possibly, the history (the past states). When the number of states is large, succinct representations are often used to compactly represent both the MDPs and the policies in a reduced amount of space. In this paper, some problems related to the size of succinctly represented policies are analyzed. Namely, it is shown that some MDPs have policies that can only be represented in space super-polynomial in the size of the MDP, unless the polynomial hierarchy collapses. This fact motivates the study of the problem of deciding whether a given MDP has a policy of a given size and reward. Since some algorithms for MDPs work by finding a succinct representation of the value function, the problem of deciding the existence of a succinct representation of a value function of a given size and reward is also considered.

Many variants of default logics exist. Two of the main differences among them arise from the choice between local and global consistency, and the choice of whether or not to accept maximally successful sets of defaults. In this paper, we characterize theories that do not depend at all on what makes the semantics different, that is, theories for which these two choices do not matter. A result that is proved for such theories holds not only for all the considered semantics, but also for every other semantics that differs from them on the two choices.

Independence -- the study of what is relevant to a given problem of reasoning -- has received an increasing attention from the AI community. In this paper, we consider two basic forms of independence, namely, a syntactic one and a semantic one. We show features and drawbacks of them. In particular, while the syntactic form of independence is computationally easy to check, there are cases in which things that intuitively are not relevant are not recognized as such. We also consider the problem of forgetting, i.e., distilling from a knowledge base only the part that is relevant to the set of queries constructed from a subset of the alphabet. While such process is computationally hard, it allows for a simplification of subsequent reasoning, and can thus be viewed as a form of compilation: once the relevant part of a knowledge base has been extracted, all reasoning tasks to be performed can be simplified.

The idea of preprocessing part of the input of a problem in order to improve efficiency has been employed by several researchers in several areas of computer science. In this article, we show sufficient conditions to prove that an intractable problem cannot be efficiently solved even allowing an exponentially long preprocessing phase. The generality of such conditions is shown by applying them to various problems coming from different fields. While the results may seem to discourage the use of compilation, we present some evidence that such negative results are useful in practice.

We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Intuitively, the space efficiency of a formalism F in representing a certain piece of knowledge A, is the size of the shortest formula of F that represents A. In this paper we assume that knowledge is either a set of propositional interpretations (models) or a set of propositional formulae (theorems). We provide a formal way of talking about the relative ability of PKR formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators and the stable model semantics for logic programs with negation. One interesting result is that formalisms with the same time complexity do not necessarily belong to the same space efficiency class.

Several methods have been proposed as an attempt to deal with dynamically changing scenarios. From a computational point of view, different formalisms have different computational properties. In this article we consider knowledge bases represented as sets of Horn clauses. The importance of this case is twofold: first, inference is polynomial, thus tractable; second, Horn clauses represents causal relations between facts, thus they are of great practical importance, although not all propositional knowledge bases can be represented in Horn form. The complexity of Horn revision is still high, and in some cases coincides with the complexity of the general (non-Horn) case. We analyze the complexity of belief revision from the point of view of the compilation [Cadoli et al. 1999]: we study the possibility of reducing the complexity by allowing a (possibly expensive) preprocessing of part of the input of the problem. Extending the work of Cadoli et al.[1996], we consider the problem of compact representation of revision in the Horn case, i.e., given a knowledge base T and an update P (both represented by Horn clauses) decide whether T * P , the result of the revision, can be represented with a propositional formula whose size is polynomial in the size of T and P . We give this representation for all formalisms for which it exists, and we show that the existence of a compact representation is related to the possibility of decreasing the complexity of a formalism via a proprocessing.

Knowledge-based systems must be able to "intelligently" manage a large amount of information coming from different sources and at different moments in time. Intelligent systems must be able to cope with a changing world by adopting a "principled" strategy. Many formalisms have been put forward in the artificial intelligence (AI) and database (DB) literature to address this problem. Among them, belief revision is one of the most successful frameworks to deal with dynamically changing worlds. Formal properties of belief revision have been investigated by Alchourron, Gardenfors, and Makinson, who put forward a set of postulates stating the properties that a belief revision operator should satisfy. Among these properties, a basic assumption of revision is that the new piece of information is totally reliable and, therefore, must be in the revised knowledge base. Different principles must be applied when there are two different sources of information and each one has a different view of the situation-the two views contradicting each other. If we do not have any reason to consider any of the sources completely unreliable, the best we can do is to "merge" the two views in a new and consistent one, trying to preserve as much information as possible. We call this merging process arbitration. In this paper, we investigate the properties that any arbitration operator should satisfy. In the style of Alchourron, Gardenfors, and Makinson we propose a set of postulates, analyze their properties, and propose actual operators for arbitration.

Article Free Access Share on The size of a revised knowledge base Authors: Marco Cadoli Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile , Francesco M. Donini Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile , Paolo Liberatore Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile , Marco Schaerf Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile Authors Info & Claims PODS '95: Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systemsMay 1995 Pages 151–162https://doi.org/10.1145/212433.220205Published:22 May 1995Publication History 12citation198DownloadsMetricsTotal Citations12Total Downloads198Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF