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Similarity-based unification

Unification plays a central rule in Logic Programming. We ”soften” the unification process by admitting that two first order expressions can be ”similar” up to a certain degree and not necessarly identical. An extension of the classical unification theory is proposed accordingly. Indeed, in our approach, inspirated by the unification algorithm of Martelli-Montanari, the systems of equations go through a series of ”sound” transformations until a solvable form is found yielding a substitution that is proved to be a most general extended unifier for the given system of equations.

Likelog: a logic programming language for flexible data retrieval

Starting from unification based on similarity, a logic programming system, called Likelog, (LIKEness in LOGic) is derived, thorougly relying on similarity. An operational semantics and a fix-point semantics are defined, using an extension principle for fuzzy operators. The two approaches are proved to be related and a fuzzy extension of the Ieast Herbrand model is given. One of the principal feature of such a logic programming system is to allow flexible information retrieval in deductive data bases.

Fuzzy subgroups and similarities

Abstract Given a set S, we show that there is a strict relation between the notion of similarity on S and the one of fuzzy subgroup of transformations in S . Such a relation enables us to extablish a connection between fuzzy subgroups and distances.

A Similarity-Based Resolution Rule

We propose an extension of the resolution rule as the core of a logic programming language based on similarity. Starting from a fuzzy unification algorithm described in Ref. 2 and then extended in Ref. 10, we introduce a fuzzy resolution rule, based on an extended most general unifier supplied by the extended unification algorithm. In our approach, unification fades into a unification degree because of a similarity introduced in a first‐order language. Intuitively, the unification degree of a set of first‐order terms is the cost one has to pay to consider these terms as equal. For this reason, our extension of the resolution is more structured than its classic counterpart;that is, when the empty clause is reached, in addition to a computed answer, a set of conditions is also determined. We give both the operational and fixed‐point semantics of our extended logic programming language, and we prove their equivalence.

Constraint-based protocols for distributed problem solving

Abstract Distributed Problem Solving (DPS) approaches decompose problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for solving problems characterized by many interdependencies among subproblems in the context of parallel and distributed architectures. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS where constraints define local problem solving and the exchange of information among agents declaratively. To optimize DPS, the protocol for constraint communication must be tuned to the specific kind of DPS problem and the characteristics of the underlying system architecture. In this paper, we provide a formal framework for modeling different problems and we show how the framework applies to simple yet generalizable examples.

Grasping Infinity by Finite Sets

We show that the existence of an infinite set can be reduced to the existence of finite sets “as big as we will”, provided that a multivalued extension of the relation of equipotence is admitted. In accordance, we modelize the notion of infinite set by a fuzzy subset representing the class of (finite) wide sets.

Boosting concept discovery in collective intelligences

Collective intelligence derives from the connection and the interaction of multiple, distributed, independent intelligent units via a network, such as, typically, a digital data network. As collective intelligences are effectively making their way into reality in consequence of ubiquitous digital communication, the opportunity and the challenge arise of extending their basic architecture in order to support higher thought-processes analogous to those characterizing human intelligences. We address here specifically the process of conceptual abstraction, namely the discovery of new concepts and ideas, and, to this purpose, we introduce the general functional notion of cognitive prosthesis supporting the implementation of a given thought-process in a collective intelligence. Since there exists a direct relationship between concept discovery and innovation in human intelligences, we point out how analogous innovation capabilities can now be supported for collective intelligences, with direct applications to Web-based innovation of products and services.

Equalizing the Structures of Web Communities in Ontology Development Tools

In this paper we face some relevant issues on the relations between web communities and ontologies. We build an operator that constructs a weak Web Community, according to the definition given in [16],starting from a seed of web sites. The necessity of such an operator is derived from a problem arisen in the model developed in [3], in which some relevant concepts in automotive oriented ontology were not given a corresponding Web community. This fact –if not considered- can bring automatic ontology development ([9,18]) to some non-correct results. In this work we define and analyze a new operator, called Com, with the tools furnished by the method of parametrization ([8,15]) and we find that, given a seed S and the induced graph I(S), the community generated by our operator is monotonic with respect to clustering and is denser than the original graph I(S).

A measure of information reuse to compare distributed protocols

In the framework of distributed problem solving we introduce two communication protocols, the request-subrequest protocol and the local-caching protocol, which are based on the hypothesis of minimal and maximal reuse of available information, respectively. The optimal solution of a distributed problem can be reached according to different protocols. The notion of measure of reuse related to each protocol has been formally introduced to provide a criterion to compare and to choose the right protocol.

Computational Models for Information Reuse

Telecom Italia, Divisione Ricerca e Sviluppo, ItalyEmail: arcelli@dia.unisa.itTechniques that optimize computations by reusing partial results have a long tradition in computerscience. Seen from the point of view of sequential computations, all these techniques share thecommon execution strategy of storingpartial results ina centralized datastructure, whiletheydifferas to how the results are computed, e.g. in a data-driven or constraint-driven fashion. Concurrentsystems, namely the wide variety of systems that range from fine-grained parallelism to coarse-grained distribution, add another variable into the game. In fact, information reuse is here tangledwith issues of local memory of agents and inter-agent communication. Thus, optimal strategies forinformation reuse directly affect agent configurations as well as agent communication protocols andstrictly depend on those morphological aspects of the computational domain related to the sharingof structures among different data values. In this paper, we define a formal framework suitable forthe study of information reuse from the point of view of concurrent systems. The main result ofour work is in the identification of two distinct morphological features of computational domains,namely recursively replicated structures and structure copying. These features induce two differentforms of information reuse that can be optimized, respectively, by solipsistic agents with large localmemory and by large bandwidth networks of collaborative agents.Received April 21, 1998; revised September 14, 1999