262 research outputs found
The PITA System: Tabling and Answer Subsumption for Reasoning under Uncertainty
Many real world domains require the representation of a measure of
uncertainty. The most common such representation is probability, and the
combination of probability with logic programs has given rise to the field of
Probabilistic Logic Programming (PLP), leading to languages such as the
Independent Choice Logic, Logic Programs with Annotated Disjunctions (LPADs),
Problog, PRISM and others. These languages share a similar distribution
semantics, and methods have been devised to translate programs between these
languages. The complexity of computing the probability of queries to these
general PLP programs is very high due to the need to combine the probabilities
of explanations that may not be exclusive. As one alternative, the PRISM system
reduces the complexity of query answering by restricting the form of programs
it can evaluate. As an entirely different alternative, Possibilistic Logic
Programs adopt a simpler metric of uncertainty than probability. Each of these
approaches -- general PLP, restricted PLP, and Possibilistic Logic Programming
-- can be useful in different domains depending on the form of uncertainty to
be represented, on the form of programs needed to model problems, and on the
scale of the problems to be solved. In this paper, we show how the PITA system,
which originally supported the general PLP language of LPADs, can also
efficiently support restricted PLP and Possibilistic Logic Programs. PITA
relies on tabling with answer subsumption and consists of a transformation
along with an API for library functions that interface with answer subsumption
Introduzione all'Intelligenza Artificiale
The paper presents an introduction to Artificial Intelligence (AI) in an
accessible and informal but precise form. The paper focuses on the algorithmic
aspects of the discipline, presenting the main techniques used in AI systems
groped in symbolic and subsymbolic. The last part of the paper is devoted to
the discussion ongoing among experts in the field and the public at large about
on the advantages and disadvantages of AI and in particular on the possible
dangers. The personal opinion of the author on this subject concludes the
paper.
-----
L'articolo presenta un'introduzione all'Intelligenza Artificiale (IA) in
forma divulgativa e informale ma precisa. L'articolo affronta prevalentemente
gli aspetti informatici della disciplina, presentando le principali tecniche
usate nei sistemi di IA divise in simboliche e subsimboliche. L'ultima parte
dell'articolo presenta il dibattito in corso tra gli esperi e il pubblico su
vantaggi e svantaggi dell'IA e in particolare sui possibili pericoli.
L'articolo termina con l'opinione dell'autore al riguardo.Comment: 27 pages, in Italia
Quantum Weighted Model Counting
In Weighted Model Counting (WMC) we assign weights to Boolean literals and we
want to compute the sum of the weights of the models of a Boolean function
where the weight of a model is the product of the weights of its literals. WMC
was shown to be particularly effective for performing inference in graphical
models, with a complexity of where is the number of variables and
is the treewidth. In this paper, we propose a quantum algorithm for
performing WMC, Quantum WMC (QWMC), that modifies the quantum model counting
algorithm to take into account the weights. In turn, the model counting
algorithm uses the algorithms of quantum search, phase estimation and Fourier
transform. In the black box model of computation, where we can only query an
oracle for evaluating the Boolean function given an assignment, QWMC solves the
problem approximately with a complexity of oracle
calls while classically the best complexity is , thus achieving a
quadratic speedup
SWISH: SWI-Prolog for Sharing
Recently, we see a new type of interfaces for programmers based on web
technology. For example, JSFiddle, IPython Notebook and R-studio. Web
technology enables cloud-based solutions, embedding in tutorial web pages,
atractive rendering of results, web-scale cooperative development, etc. This
article describes SWISH, a web front-end for Prolog. A public website exposes
SWI-Prolog using SWISH, which is used to run small Prolog programs for
demonstration, experimentation and education. We connected SWISH to the
ClioPatria semantic web toolkit, where it allows for collaborative development
of programs and queries related to a dataset as well as performing maintenance
tasks on the running server and we embedded SWISH in the Learn Prolog Now!
online Prolog book.Comment: International Workshop on User-Oriented Logic Programming (IULP
2015), co-located with the 31st International Conference on Logic Programming
(ICLP 2015), Proceedings of the International Workshop on User-Oriented Logic
Programming (IULP 2015), Editors: Stefan Ellmauthaler and Claudia Schulz,
pages 99-113, August 201
MAP Inference in Probabilistic Answer Set Programs
Reasoning with uncertain data is a central task in artificial intelligence.
In some cases, the goal is to find the most likely assignment to a subset of random variables, named query variables, while some other variables are observed.
This task is called Maximum a Posteriori (MAP).
When the set of query variables is the complement of the observed variables, the task goes under the name of Most Probable Explanation (MPE).
In this paper, we introduce the definitions of cautious and brave MAP and MPE tasks in the context of Probabilistic Answer Set Programming under the credal semantics and provide an algorithm to solve them.
Empirical results show that the brave version of both tasks is usually faster to compute.
On the brave MPE task, the adoption of a state-of-the-art ASP solver makes the computation much faster than a naive approach based on the enumeration of all the worlds
Lifted Variable Elimination for Probabilistic Logic Programming
Lifted inference has been proposed for various probabilistic logical
frameworks in order to compute the probability of queries in a time that
depends on the size of the domains of the random variables rather than the
number of instances. Even if various authors have underlined its importance for
probabilistic logic programming (PLP), lifted inference has been applied up to
now only to relational languages outside of logic programming. In this paper we
adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the
problem of computing the probability of queries to probabilistic logic programs
under the distribution semantics. In particular, we extend the Prolog Factor
Language (PFL) to include two new types of factors that are needed for
representing ProbLog programs. These factors take into account the existing
causal independence relationships among random variables and are managed by the
extension to variable elimination proposed by Zhang and Poole for dealing with
convergent variables and heterogeneous factors. Two new operators are added to
GC-FOVE for treating heterogeneous factors. The resulting algorithm, called
LP for Lifted Probabilistic Logic Programming, has been implemented by
modifying the PFL implementation of GC-FOVE and tested on three benchmarks for
lifted inference. A comparison with PITA and ProbLog2 shows the potential of
the approach.Comment: To appear in Theory and Practice of Logic Programming (TPLP). arXiv
admin note: text overlap with arXiv:1402.0565 by other author
Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities
Type 1 statements were introduced by Halpern in 1990 with the goal to represent statistical information about a domain of interest.
These are of the form ''x of the elements share the same property''.
The recently proposed language PASTA (Probabilistic Answer set programming for STAtistical probabilities) extends Probabilistic Logic Programs under the Distribution Semantics and allows the definition of this type of statements.
To perform exact inference, PASTA programs are converted into probabilistic answer set programs under the Credal Semantics.
However, this algorithm is infeasible for scenarios when more than a few random variables are involved.
Here, we propose several algorithms to perform both conditional and unconditional approximate inference in PASTA programs and test them on different benchmarks.
The results show that approximate algorithms scale to hundreds of variables and thus can manage real world domains
Exploiting Uncertainty for Querying Inconsistent Description Logics Knowledge Bases
The necessity to manage inconsistency in Description Logics Knowledge
Bases~(KBs) has come to the fore with the increasing importance gained by the
Semantic Web, where information comes from different sources that constantly
change their content and may contain contradictory descriptions when considered
either alone or together. Classical reasoning algorithms do not handle
inconsistent KBs, forcing the debugging of the KB in order to remove the
inconsistency. In this paper, we exploit an existing probabilistic semantics
called DISPONTE to overcome this problem and allow queries also in case of
inconsistent KBs. We implemented our approach in the reasoners TRILL and BUNDLE
and empirically tested the validity of our proposal. Moreover, we formally
compare the presented approach to that of the repair semantics, one of the most
established semantics when considering DL reasoning tasks
- …