Glauber De Bona

Probabilistic satisfiability: logic-based algorithms and phase transition

In this paper, we study algorithms for probabilistic satisfiability (PSAT), an NP-complete problem, and their empiric complexity distribution. We define a PSAT normal form, based on which we propose two logic-based algorithms: a reduction of normal form PSAT instances to SAT, and a linearalgebraic algorithmwith a logic-based column generation strategy. We conclude that both algorithms present a phase transition behaviour and that the latter has a much better performance.

Measuring inconsistency in probabilistic logic

Consistency, independence and continuity are incompatible postulates.Minimal inconsistent sets are not suitable to analyze probabilistic incon-sistencies.Independence can be weakened considering the underlying consolidation process.Inconsistency and incoherence measures based on distances and Dutch books coincide. Inconsistency measures have been proposed as a way to manage inconsistent knowledge bases in the AI community. To deal with inconsistencies in the context of conditional probabilistic logics, rationality postulates and computational efficiency have driven the formulation of inconsistency measures. Independently, investigations in formal epistemol-ogy have used the betting concept of Dutch book to measure an agents degree of incoherence. In this paper, we show the impossibility of joint satisfiability of the proposed postulates, proposing to replace them by more suitable ones. Thus we reconcile the rationality postulates for inconsistency measures in probabilistic bases and show that several inconsistency measures suggested in the literature and computable with linear programs satisfy the reconciled postulates. Additionally, we give an interpretation for these feasible measures based on the formal epistemology concept of Dutch book, bridging the views of two so far separate communities in AI and Philosophy. In particular, we show that incoherence degrees in formal epistemology may lead to novel approaches to inconsistency measures in the AI view.

Towards classifying propositional probabilistic logics

This paper examines two aspects of propositional probabilistic logics: the nesting of probabilistic operators, and the expressivity of probabilistic assessments. We show that nesting can be eliminated when the semantics is based on a single probability measure over valuations; we then introduce a classification for probabilistic assessments, and present novel results on their expressivity. Logics in the literature are categorized using our results on nesting and on probabilistic expressivity.

Graded Incoherence for Accuracy-Firsters

This article investigates the relationship between two evaluative claims about agents’ degrees of belief: (i) that it is better to have more rather than less accurate degrees of belief and (ii) that it is better to have less rather than more probabilistically incoherent degrees of belief. We show that, for suitable combinations of inaccuracy measures and incoherence measures, both claims are compatible, although not equivalent; moreover, certain ways of becoming less incoherent always guarantee improvements in accuracy. Incompatibilities between particular incoherence and inaccuracy measures can be exploited to argue against particular ways of measuring either inaccuracy or incoherence.

Probabilistic satisfiability: algorithms with the presence and absence of a phase transition

We study algorithms for probabilistic satisfiability (PSAT), an NP-complete problem that is central to logic-probabilisti reasoning, focusing on the presence and absence of a phase transition phenomenon for each algorithm. Our study starts by defining a PSAT normal form, on which all algorithms are based. The proposed algorithms consist of several forms of reductions of PSAT to classical propositional satisfiability (SAT). The first algorithm is a canonical reduction of PSAT instances to SAT instances; three other algorithms are reductions to linear optimization with distinct column generation procedures, namely on auxiliary calls to SAT, weighted MAXSAT or SMT solvers. Theoretical and practical limitations of each algorithm are discussed. Several implementations were developed and compared by means of experiments using randomly generated input problems. Some of the implementations are shown to present a phase transition behavior. We show that variations of these algorithms may lead to the partial occlusion of the phase transition phenomenon and discuss the reasons for this change ofc practical behavior.

A logic based algorithm for solving probabilistic satisfiability

This paper presents a study of the relationship between probabilistic reasoning and deductive reasoning, in propositional format. We propose an algorithm to solve probabilistic satisfiability (PSAT) based on the study of its logical properties. Each iteration of the algorithm generates in polynomial time a classical (non-probabilistic) formula that is submitted to a SAT-oracle. This strategy is a Turing reduction of PSAT into SAT. We demonstrate the correctness and termination of the algorithm.

On the Coherence of Probabilistic Relational Formalisms

There are several formalisms that enhance Bayesian networks by including relations amongst individuals as modeling primitives. For instance, Probabilistic Relational Models (PRMs) use diagrams and relational databases to represent repetitive Bayesian networks, while Relational Bayesian Networks (RBNs) employ first-order probability formulas with the same purpose. We examine the coherence checking problem for those formalisms; that is, the problem of guaranteeing that any grounding of a well-formed set of sentences does produce a valid Bayesian network. This is a novel version of de Finetti’s problem of coherence checking for probabilistic assessments. We show how to reduce the coherence checking problem in relational Bayesian networks to a validity problem in first-order logic augmented with a transitive closure operator and how to combine this logic-based approach with faster, but incomplete algorithms.

Generalized probabilistic satisfiability through integer programming

BackgroundThis paper studies the generalized probabilistic satisfiability (GPSAT) problem, where the probabilistic satisfiability (PSAT) problem is extended by allowing Boolean combinations of probabilistic assertions and nested probabilistic formulas.MethodsWe introduce a normal form for this problem and show that both nesting of probabilities and multi-agent probabilities do not increase the expressivity of GPSAT. An algorithm to solve GPSAT instances in the normal form via mixed integer linear programming is proposed.ResultsThe implementation of the algorithm is used to explore the complexity profile of GPSAT, and it shows evidence of phase-transition phenomena.ConclusionsEven though GPSAT is considerably more expressive than PSAT, it can be handled using integer linear programming techniques.

Generalized Probabilistic Satisfiability

This paper studies the Generalized Probabilistic Satisfiability (GPSAT) problem, where the probabilistic satisfiability problem is extended by allowing Boolean combinations of probabilistic assertions and nested probabilistic formulas. We introduce a normal form for this problem and show that nesting of probabilities does not increase the expressivity in GPSAT. An algorithm to solve GPSAT problems via mixed integer programming is proposed, and its implementation shows evidence of phase-transition phenomena.

A refuted conjecture on probabilistic satisfiability

In this paper, we investigate the Probabilistic Satisfiability Problem, and its relation with the classical Satisfiability Problem, looking for a possible polynomial-time reduction. For this, we present an Atomic Normal Form to the probabilistic satisfiability problem and then we define a Probabilistic Entailment relation, showing its inherent properties. At the end, we enunciate and refute a conjecture that could lead to the desired polynomial-time reduction.