16 research outputs found
Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming
Domain-specific heuristics are an essential technique for solving
combinatorial problems efficiently. Current approaches to integrate
domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory
when dealing with heuristics that are specified non-monotonically on the basis
of partial assignments. Such heuristics frequently occur in practice, for
example, when picking an item that has not yet been placed in bin packing.
Therefore, we present novel syntax and semantics for declarative specifications
of domain-specific heuristics in ASP. Our approach supports heuristic
statements that depend on the partial assignment maintained during solving,
which has not been possible before. We provide an implementation in ALPHA that
makes ALPHA the first lazy-grounding ASP system to support declaratively
specified domain-specific heuristics. Two practical example domains are used to
demonstrate the benefits of our proposal. Additionally, we use our approach to
implement informed} search with A*, which is tackled within ASP for the first
time. A* is applied to two further search problems. The experiments confirm
that combining lazy-grounding ASP solving and our novel heuristics can be vital
for solving industrial-size problems
Defeasible RDFS via Rational Closure
In the field of non-monotonic logics, the notion of Rational Closure (RC) is
acknowledged as a prominent approach. In recent years, RC has gained even more
popularity in the context of Description Logics (DLs), the logic underpinning
the semantic web standard ontology language OWL 2, whose main ingredients are
classes and roles. In this work, we show how to integrate RC within the triple
language RDFS, which together with OWL2 are the two major standard semantic web
ontology languages. To do so, we start from , which is the logic
behind RDFS, and then extend it to , allowing to state that two
entities are incompatible. Eventually, we propose defeasible via
a typical RC construction. The main features of our approach are: (i) unlike
most other approaches that add an extra non-monotone rule layer on top of
monotone RDFS, defeasible remains syntactically a triple
language and is a simple extension of by introducing some new
predicate symbols with specific semantics. In particular, any RDFS
reasoner/store may handle them as ordinary terms if it does not want to take
account for the extra semantics of the new predicate symbols; (ii) the
defeasible entailment decision procedure is build on top of the
entailment decision procedure, which in turn is an extension of
the one for via some additional inference rules favouring an
potential implementation; and (iii) defeasible entailment can be
decided in polynomial time.Comment: 47 pages. Preprint versio
Un procesador de expresiones epistémicas en programas lógicos
[Resumen] En este proyecto se ha desarrollado la herramienta eclingo que calcula los modelos de un programa lógico con expresiones epistémicas. Estas expresiones suponen una ampliación del lenguaje declarativo Answer Set Programming (ASP), ampliamente usado en el área de Representación del Conocimiento en Inteligencia Artificial. En ASP, un problema de búsqueda se representa en términos de un programa lógico, y las soluciones al problema se obtienen a partir de los modelos (answer sets) del programa. Las expresiones epistémicas admitidas por eclingo permiten razonar sobre hechos que están presentes en todos los answer sets o en alguno de ellos, lo que permite razonamiento sobre incertidumbre y conocimiento parcial. La eficiencia de eclingo se ha evaluado a través de un estudio comparativo frente a otra herramienta de características similares, ofreciendo unos resultados muy positivos que la sitúan como una alternativa competitiva dentro del estado del arte.[Abstract] The developed tool, eclingo, computes the models of logic programs with epistemic expressions. These expressions represent an extension of the declarative language Answer Set Programming, widely used in the area of Knowledge Representation in Artificial Intelligence. In ASP, a search problem is represented in terms of a logic program, and solutions to the problem are obtained from the models (answer sets) of this program. The epistemic expressions accepted by eclingo allow reasoning about facts that are present in all answer sets or in some of them, which enables reasoning about uncertainty and partial knowledge. The efficiency of eclingo has been evaluated through a comparative study against another tool with similar characteristics, offering very positive results that place it as a competitive alternative within the state of the art.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2018/201
Automatically selecting patients for clinical trials with justifications
Clinical trials are human research studies that are used to evaluate the effectiveness
of a surgical, medical, or behavioral intervention. They have been widely used by researchers
to determine whether a new treatment, such as a new medication, is safe and
effective in humans. A clinical trial is frequently performed to determine whether a new
treatment is more successful than the current treatment or has less harmful side effects.
However, clinical trials have a high failure rate. One method applied is to find patients
based on patient records. Unfortunately, this is a difficult process. This is because this
process is typically performed manually, making it time-consuming and error-prone.
Consequently, clinical trial deadlines are often missed, and studies do not move forward.
Time can be a determining factor for success. Therefore, it would be advantageous to have
automatic support in this process. Since it is also important to be able to validate whether
the patients were selected correctly for the trial, avoiding eventual health problems, it
would be important to have a mechanism to present justifications for the selected patients.
In this dissertation, we present one possible solution to solve the problem of patient
selection for clinical trials. We developed the necessary algorithms and created a simple
and intuitive web application that features the selection of patients for clinical trials automatically.
This was achieved by combining knowledge expressed in different formalisms.
We integrated medical knowledge using ontologies, with criteria that were expressed
using nonmonotonic rules. To address the validation procedure automatically, we developed
a mechanism that generates the justifications for each selection together with the
results of the patients who were selected.
In the end, it is expected that a user can easily enter a set of trial criteria, and the
application will generate the results of the selected patients and their respective justifications,
based on the criteria inserted, medical information and a database of patient
information.Os ensaios clínicos são estudos de pesquisa em humanos, utilizados para avaliar a
eficácia de uma intervenção cirúrgica, médica ou comportamental. Estes estudos, têm
sido amplamente utilizados pelos investigadores para determinar se um novo tratamento,
como é o caso de um novo medicamento, é seguro e eficaz em humanos. Um ensaio clínico
é realizado frequentemente, para determinar se um novo tratamento tem mais sucesso
do que o tratamento atual ou se tem menos efeitos colaterais prejudiciais.
No entanto, os ensaios clínicos têm uma taxa de insucesso alta. Um método aplicado
é encontrar pacientes com base em registos. Infelizmente, este é um processo difícil.
Isto deve-se ao facto deste processo ser normalmente realizado à mão, o que o torna
demorado e propenso a erros. Consequentemente, o prazo dos ensaios clínicos é muitas
vezes ultrapassado e os estudos acabam por não avançar. O tempo pode ser por vezes um
fator determinante para o sucesso. Seria então vantajoso ter algum apoio automático neste
processo. Visto que também seria importante validar se os pacientes foram selecionados
corretamente para o ensaio, evitando até eventuais problemas de saúde, seria importante
ter um mecanismo que apresente justificações para os pacientes selecionados.
Nesta dissertação, apresentamos uma possível solução para resolver o problema da
seleção de pacientes para ensaios clínicos, através da criação de uma aplicação web, intuitiva
e fácil de utilizar, que apresenta a seleção de pacientes para ensaios clínicos de
forma automática. Isto foi alcançado através da combinação de conhecimento expresso
em diferentes formalismos. Integrámos o conhecimento médico usando ontologias, com
os critérios que serão expressos usando regras não monotónicas. Para tratar do processo
de validação, desenvolvemos um mecanismo que gera justificações para cada seleção
juntamente com os resultados dos pacientes selecionados.
No final, é esperado que o utilizador consiga inserir facilmente um conjunto de critérios
de seleção, e a aplicação irá gerar os resultados dos pacientes selecionados e as
respetivas justificações, com base nos critérios inseridos, informações médicas e uma base
de dados com informações dos pacientes
Learning programs by learning from failures
We describe an inductive logic programming (ILP) approach called learning
from failures. In this approach, an ILP system (the learner) decomposes the
learning problem into three separate stages: generate, test, and constrain. In
the generate stage, the learner generates a hypothesis (a logic program) that
satisfies a set of hypothesis constraints (constraints on the syntactic form of
hypotheses). In the test stage, the learner tests the hypothesis against
training examples. A hypothesis fails when it does not entail all the positive
examples or entails a negative example. If a hypothesis fails, then, in the
constrain stage, the learner learns constraints from the failed hypothesis to
prune the hypothesis space, i.e. to constrain subsequent hypothesis generation.
For instance, if a hypothesis is too general (entails a negative example), the
constraints prune generalisations of the hypothesis. If a hypothesis is too
specific (does not entail all the positive examples), the constraints prune
specialisations of the hypothesis. This loop repeats until either (i) the
learner finds a hypothesis that entails all the positive and none of the
negative examples, or (ii) there are no more hypotheses to test. We introduce
Popper, an ILP system that implements this approach by combining answer set
programming and Prolog. Popper supports infinite problem domains, reasoning
about lists and numbers, learning textually minimal programs, and learning
recursive programs. Our experimental results on three domains (toy game
problems, robot strategies, and list transformations) show that (i) constraints
drastically improve learning performance, and (ii) Popper can outperform
existing ILP systems, both in terms of predictive accuracies and learning
times.Comment: Accepted for the machine learning journa
ECHO: A hierarchical combination of classical and multi-agent epistemic planning problems
The continuous interest in Artificial Intelligence (AI) has brought, among other things, the development of several scenarios where multiple artificial entities interact with each other. As for all the other autonomous settings, these multi-agent systems require orchestration. This is, generally, achieved through techniques derived from the vast field of Automated Planning. Notably, arbitration in multi-agent domains is not only tasked with regulating how the agents act, but must also consider the interactions between the agents' information flows and must, therefore, reason on an epistemic level. This brings a substantial overhead that often diminishes the reasoning process's usability in real-world situations. To address this problem, we present ECHO, a hierarchical framework that embeds classical and multi-agent epistemic (epistemic, for brevity) planners in a single architecture. The idea is to combine (i) classical; and(ii) epistemic solvers to model efficiently the agents' interactions with the (i) 'physical world'; and(ii) information flows, respectively. In particular, the presented architecture starts by planning on the 'epistemic level', with a high level of abstraction, focusing only on the information flows. Then it refines the planning process, due to the classical planner, to fully characterize the interactions with the 'physical' world. To further optimize the solving process, we introduced the concept of macros in epistemic planning and enriched the 'classical' part of the domain with goal-networks. Finally, we evaluated our approach in an actual robotic environment showing that our architecture indeed reduces the overall computational time