Abstract Diagnosis for Timed Concurrent Constraint programs

The Timed Concurrent Constraint Language (tccp in short) is a concurrent logic language based on the simple but powerful concurrent constraint paradigm of Saraswat. In this paradigm, the notion of store-as-value is replaced by the notion of store-as-constraint, which introduces some differences w.r.t. other approaches to concurrency. In this paper, we provide a general framework for the debugging of tccp programs. To this end, we first present a new compact, bottom-up semantics for the language that is well suited for debugging and verification purposes in the context of reactive systems. We also provide an abstract semantics that allows us to effectively implement debugging algorithms based on abstract interpretation. Given a tccp program and a behavior specification, our debugging approach automatically detects whether the program satisfies the specification. This differs from other semiautomatic approaches to debugging and avoids the need to provide symptoms in advance. We show the efficacy of our approach by introducing two illustrative examples. We choose a specific abstract domain and show how we can detect that a program is erroneous.Comment: 16 page

An Effective Fixpoint Semantics for Linear Logic Programs

In this paper we investigate the theoretical foundation of a new bottom-up semantics for linear logic programs, and more precisely for the fragment of LinLog that consists of the language LO enriched with the constant 1. We use constraints to symbolically and finitely represent possibly infinite collections of provable goals. We define a fixpoint semantics based on a new operator in the style of Tp working over constraints. An application of the fixpoint operator can be computed algorithmically. As sufficient conditions for termination, we show that the fixpoint computation is guaranteed to converge for propositional LO. To our knowledge, this is the first attempt to define an effective fixpoint semantics for linear logic programs. As an application of our framework, we also present a formal investigation of the relations between LO and Disjunctive Logic Programming. Using an approach based on abstract interpretation, we show that DLP fixpoint semantics can be viewed as an abstraction of our semantics for LO. We prove that the resulting abstraction is correct and complete for an interesting class of LO programs encoding Petri Nets.Comment: 39 pages, 5 figures. To appear in Theory and Practice of Logic Programmin

Model Checking Linear Logic Specifications

The overall goal of this paper is to investigate the theoretical foundations of algorithmic verification techniques for first order linear logic specifications. The fragment of linear logic we consider in this paper is based on the linear logic programming language called LO enriched with universally quantified goal formulas. Although LO was originally introduced as a theoretical foundation for extensions of logic programming languages, it can also be viewed as a very general language to specify a wide range of infinite-state concurrent systems. Our approach is based on the relation between backward reachability and provability highlighted in our previous work on propositional LO programs. Following this line of research, we define here a general framework for the bottom-up evaluation of first order linear logic specifications. The evaluation procedure is based on an effective fixpoint operator working on a symbolic representation of infinite collections of first order linear logic formulas. The theory of well quasi-orderings can be used to provide sufficient conditions for the termination of the evaluation of non trivial fragments of first order linear logic.Comment: 53 pages, 12 figures "Under consideration for publication in Theory and Practice of Logic Programming

Transactions and updates in deductive databases

n this paper we develop a new approach providing a smooth integration of extensional updates and declarative query language for deductive databases. The approach is based on a declarative speci cation of updates in rule bodies. Updates are not executed as soon are evaluated. Instead, they are collectedand then applied to the database when the query evaluation is completed. We call this approach non-immediate update semantics. We provide a top down and equivalent bottom-up semantics which re ect the corresponding computation models. We also package set of updates into transactions and we provide a formal semantics for transactions. Then, in order to handle complex transactions, we extend the transaction language with control constructors still perserving formal semantics and semantics equivalence

Knowledge Representation with Multiple Logical Theories and Time

We present a knowledge representation framework where a collection of logic programs can be combined together by means of meta-level program composition operations. Each object-level program is composed of a collection of extended clauses, equipped with a time interval representing the time period in which they hold. The interaction between program composition operations and time yields a powerful knowledge representation language in which many applications can be naturally developed. The language is given a meta-level semantics which also provides an executable specification. Moreover, we define an abstract semantics by extending the immediate consequence operator from a single logic program to compositions of logic programs and taking into account time intervals. The operational, meta-level semantics is proven sound and complete with respect to the abstract bottom-up semantics. The approach is further extended in order to cope with the problem of reasoning over joined intervals of time. Three applications in the field of business regulations are shown

Learning Logistic Circuits

This paper proposes a new classification model called logistic circuits. On MNIST and Fashion datasets, our learning algorithm outperforms neural networks that have an order of magnitude more parameters. Yet, logistic circuits have a distinct origin in symbolic AI, forming a discriminative counterpart to probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that parameter learning for logistic circuits is convex optimization, and that a simple local search algorithm can induce strong model structures from data.Comment: Published in the Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI19