Riveret et al. have proposed a framework for probabilistic legal reasoning. Their goal is to determine the chance of winning a court case, given the probabilities of the judge accepting certain claimed facts and legal rules.

In this paper we tackle the same problem by defining and implementing a new formalism, called probabilistic argumentation logic, which can be seen as a probabilistic generalization of Nute's defeasible logic. Not only does this provide an automation of the — only hand-performed — computations in Riveret et al, it also provides a solution to one of their open problems: a method to determine the initial probabilities from a given body of precedents.

The course timetabling problem can be generally defined as the task of assigning a number of lectures to a limited set of timeslots and rooms, subject to a given set of hard and soft constraints. The modeling language for course timetabling is required to be expressive enough to specify a wide variety of soft constraints and objective functions. Furthermore, the resulting encoding is required to be extensible for capturing new constraints and for switching them between hard and soft, and to be flexible enough to deal with different formulations. In this paper, we propose to make effective use of ASP as a modeling language for course timetabling. We show that our ASP-based approach can naturally satisfy the above requirements, through an ASP encoding of the curriculum-based course timetabling problem proposed in the third track of the second international timetabling competition (ITC-2007). Our encoding is compact and human-readable, since each constraint is individually expressed by either one or two rules. Each hard constraint is expressed by using integrity constraints and aggregates of ASP. Each soft constraint S is expressed by rules in which the head is the form of penalty(S,V,C), and a violation V and its penalty cost C are detected and calculated respectively in the body. We carried out experiments on four different benchmark sets with five different formulations. We succeeded either in improving the bounds or producing the same bounds for many combinations of problem instances and formulations, compared with the previous best known bounds.

The widespread availability of large data-sets poses both an opportunity and a challenge to logic programming. A first approach is to couple a relational database with logic programming, say, a Prolog system with MySQL. While this approach does pay off in cases where the data cannot reside in main memory, it is known to introduce substantial overheads. Ideally, we would like the Prolog system to deal with large data-sets in an efficient way both in terms of memory and of processing time. Just In Time Indexing (JITI) was mainly motivated by this challenge, and can work quite well in many application.

Exo-compilation, designed to deal with large tables, is a next step that achieves very interesting results, reducing the memory footprint over two thirds. We show that combining exo-compilation with Just In Time Indexing can have significant advantages both in terms of memory usage and in terms of execution time.

An alternative path that is relevant for many applications is User-Defined Indexing (UDI). This allows the use of specialized indexing for specific applications, say the spatial indexing crucial to any spatial system. The UDI sees indexing as pluggable modules, and can naturally be combined with Exo-compilation. We do so by using UDI with exo-data, and incorporating ideas from the UDI into high-performance indexers for specific tasks.

There is an increasing interest in using logic programming to specify and implement distributed algorithms, including a variety of network applications. These are applications where data and computation are distributed among several devices and where, in principle, all the devices can exchange data and share the computational results of the group. In this paper we propose a declarative approach to distributed computing whereby distributed algorithms and communication models can be (i) specified as action theories of fluents and actions; (ii) executed as collections of distributed state machines, where devices are abstracted as (input/output) automata that can exchange messages; and (iii) analysed using existing results on connecting causal theories and Answer Set Programming. Results on the application of our approach to different classes of network protocols are also presented.

We study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots between each other, subject to the following constraints: 1) teams (and the mediator) do not know about each other's workspace or tasks (e.g., for privacy purposes); 2) every team can lend or borrow robots, but not both (e.g., transportation/calibration of robots between/for different workspaces is usually costly). We present a mathematical definition of this problem and analyze its computational complexity. We introduce a novel, logic-based method to solve this problem, utilizing action languages and answer set programming for representation, and the state-of-the-art ASP solvers for reasoning. We show the applicability and usefulness of our approach by experiments on various scenarios of responsive and energy-efficient cognitive factories.

We describe a compact serialization algorithm mapping Prolog terms to natural numbers of bit-sizes proportional to the memory representation of the terms. The algorithm is a ‘no bit lost’ bijection, as it associates to each Prolog term a unique natural number and each natural number corresponds to a unique syntactically well-formed term.

To avoid an exponential explosion resulting from bijections mapping term trees to natural numbers, we separate the symbol content and the syntactic skeleton of a term that we serialize compactly using a ranking algorithm for Catalan families.

A novel algorithm for the generalized Cantor bijection between ${\mathbb{N}$ and ${\mathbb{N}$k is used in the process of assigning polynomially bounded Gödel numberings to various data objects involved in the translation.

Several extensions of the stable model semantics are available to describe ‘intensional’ functions—functions that can be described in terms of other functions and predicates by logic programs. Such functions are useful for expressing inertia and default behaviors of systems, and can be exploited for alleviating the grounding bottleneck involving functional fluents. However, the extensions were defined in different ways under different intuitions. In this paper we provide several reformulations of the extensions, and note that they are in fact closely related to each other and coincide on large syntactic classes of logic programs.

Existential rules are Datalog rules extended with existential quantifiers in rule-heads. Three fundamental restriction paradigms that have been studied for ensuring decidability of query answering under existential rules are weak-acyclicity, guardedness and stickiness. Towards the identification of even more expressive decidable languages, several attempts have been conducted to consolidate weak-acyclicity with the other two paradigms. However, it is not clear how guardedness and stickiness can be merged; this is the subject of this paper. A powerful and flexible condition, called tameness, is proposed, which allows us to consolidate in an elegant and uniform way guardedness with stickiness.