Learn More
> The research on modular logic programming has evolved along two diierent directions during the past decade. Various papers have focused primarily on the problems of programming-in-the-large. They have proposed module systems equipped with compositional operators for building programs as combinations of separate and independent components. Other proposals(More)
In Constraint Satisfaction Problems (CSPs) values belonging to variable domains should be completely known before the constraint propagation process starts. In many applications, however, the acquisition of domain values is a computational expensive process or some domain values could not be available at the beginning of the computation. For this purpose,(More)
This paper addresses the 3D object recognition problem modelled as a Constraint Satisfaction Problem. In this setting, each object view can be modelled as a constraint graph where nodes are object parts and constraints are topological and geometrical relationships among them. By modelling the problem as a CSP, we can recognize an object when all constraints(More)
SCIFF is a framework thought to specify and verify interaction in open agent societies. The SCIFF language is equipped with a semantics based on abductive logic programming; SCIFF's operational component is a new abductive logic programming proof procedure, also named SCIFF, for reasoning with expectations in dynamic environments. In this article we present(More)
In open societies of agents, where agents are autonomous and heterogeneous, it is not realistic to assume that agents will always act so as to comply to interaction protocols. Thus, the need arises for a formalism to specify constraints on agent interaction , and for a tool able to observe and check for agent compliance to interaction protocols. In this(More)
The management of business processes has recently received a lot of attention. One of the most interesting problems is the description of a process model in a language that allows the checking of the compliance of a process execution (or trace) to the model. In this paper we propose a language for the representation of process models that is inspired to the(More)
In this work we propose an approach for the automatic discovery of logic-based models starting from a set of process execution traces. The approach is based on a modified Inductive Logic Programming algorithm, capable of learning a set of declarative rules. The advantage of using a declarative description is twofold. First, the process is represented in an(More)
We discuss the adoption of a three-valued setting for inductive concept learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce, ambiguous, or downright contradictory information. In a three-valued setting, we learn a deenition for both the target(More)