Search

En: Programa del CSIC para el estudio del síndrome tóxico. Madrid : Consejo Superior de Investigaciones Científicas, 1983.

The problem of on-line planning in partially observable settings involves two problems: keeping track of beliefs about the environment and selecting actions for achieving goals. While the two problems are computationally intractable in the worst case, significant progress has been achieved in recent years through the use of suitable reductions. In particular, the state-of-the-art CLG planner is based on a translation that maps deterministic partially observable problems into fully observable non-deterministic ones. The translation, which is quadratic in the number of problem fluents and gets rid of the belief tracking problem, is adequate for most benchmarks, and it is in fact complete for problems that have width 1. The more recent K-replanner uses translations that are linear, one for keeping track of beliefs and the other for selecting actions using off-the-shelf classical planners. As a result, the K-replanner scales up better but it is not as general. In this work, we combine the benefits of the two approaches - the scope of the CLG planner and the efficiency of the Kreplanner. The new planner, called LW1, is based on a translation that is linear but complete for width-1 problems. The scope and scalability of the new planner is evaluated experimentally by considering the existing benchmarks and new problems.

The problem of belief tracking in the presence of stochastic actions and observations is pervasive and yet computationally intractable. In this work we show however that probabilistic beliefs can be maintained in factored form exactly and efficiently across a number of causally closed beams, when the state variables that appear in more than one beam obey a form of backward determinism. Since computing marginals from the factors is still computationally intractable in general, and variables appearing in several beams are not always backward-deterministic, the basic formulation is extended with two approximations: forms of belief propagation for computing marginals from factors, and sampling of non-backward-deterministic variables for making such variables backward-deterministic given their sampled history. Unlike, Rao-Blackwellized particle-filtering, the sampling is not used for making inference tractable but for making the factorization sound. The resulting algorithm involves sampling and belief propagation or just one of them as determined by the structure of the model., Proceedings IJCAI-13

In the presence of non-admissible heuristics, A* and other best-first algorithms can be converted into anytime optimal algorithms over OR graphs, by simply continuing the search after the first solution is found. The same trick, however, does not work for best-first algorithms over AND/OR graphs, that must be able to expand leaf nodes of the explicit graph that are not necessarily part of the best partial solution. Anytime optimal variants of AO* must thus address an exploration-exploitation tradeoff: they cannot just ”exploit”, they must keep exploring as well. In this work, we develop one such variant of AO* and apply it to finite-horizon MDPs. This Anytime AO* algorithm eventually delivers an optimal policy while using non-admissible random heuristics that can be sampled, as when the heuristic is the cost of a base policy that can be sampled with rollouts. We then test Anytime AO* for action selection over large infinite-horizon MDPs that cannot be solved with existing off-line heuristic search and dynamic programming algorithms, and compare it with UCT.

The pseudotumor cerebri is characterized by increased intracranial pressure in the absence of a space occupying lesions or obstruction to the circulation of cerebrospinal fluid (CSF). Increased pressure but normal composition of the CSF is the diagnostic criterion. Ear infections and other infections such as sinusitis have been cited as possible, but rare, causes.A 12 year old girl presented with diplopia 10 days after starting treatment for sinusitis. On examination there was paresia of the VI cranial nerve to the left eye and marked bilateral papilledema. She had normal visual acuity with visual field measurements showing increased blind spot field in both eyes. Resonance studies only showed maxillary sinusitis. CSF pressure was 35 cm H2O, with 40 lymphomonocytes and normal biochemical findings. After 15 cm of CSF had been removed treatment was started with acetazolamide, dexamethasone and cefotaxime. A week later the diplopia had disappeared and the CSF pressure was 25, with no cells present. Two months later the visual fields and fundus oculi were normal. After follow-up for one year there was no recurrence.We describe a case of pseudotumor cerebri associated with maxillary sinusitis. We do not know whether this is a chance association or whether there was a pathophysiological basis. The possibility should be considered so as to treat the cause or predisposing factor. The initial CSF pleocytosis would support the possibility of a relationship of the pseudotumor with an infectious condition (meningeal irritation or para-infectious pleocytosis meningeal irritation or para-infections pleocytosis in the context of an ENT infection), which might have precipitated the problem by interfering with the reabsorption of CSF.

Finite-state and memoryless controllers are simple action selection mechanisms widely used in domains such as video-games and mobile robotics. Memoryless controllers stand for functions that map observations into actions, while finite-state controllers generalize memoryless ones with a finite amount of memory. In contrast to the policies obtained from MDPs and POMDPs, finite-state controllers have two advantages: they are often extremely compact, involving a small number of controller states or none at all, and they are general, applying to many problems and not just one. A limitation of finite-state controllers is that they must be written by hand. In this work, we address this limitation, and develop a method for deriving finite-state controllers automatically from models. These models represent a class of contingent problems where actions are deterministic and some fluents are observable. The problem of deriving a controller from such models is converted into a conformant planning problem that is solved using classical planners, taking advantage of a complete translation introduced recently. The controllers derived in this way are 'general' in the sense that they do not solve the original problem only, but many variations as well, including changes in the size of the problem or in the uncertainty of the initial situation and action effects. Experiments illustrating the derivation of such controllers are presented.

Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. We present theoretical results on the relation between existing heuristics and experimental results that demonstrate the potential of the proposed framework.

Rewriting is widely used to optimise owl:sameAs reasoning in materialisation based OWL 2 RL systems. We investigate issues related to both the correctness and efficiency of rewriting, and present an algorithm that guarantees correctness, improves efficiency, and can be effectively parallelised. Our evaluation shows that our approach can reduce reasoning times on practical data sets by orders of magnitude.

We describe the hsp2.0 planning algorithm that entered the second planning contest held at the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00). hsp2.0 is a domain-independent planning algorithm that implements the family of heuristic search planners that are characterized by the state space that is searched (either progression or regression space), the search algorithm used (variants of best-first search), and the heuristic function extracted from the problem representation. This general planner implements a scheduler that tries different variants concurrently with different (time) resource bounds. We also describe how hsp2.0 can be used as an optimal (and near-optimal) planning algorithm and compare its performance with two other optimal planners, stan and blackbox.

Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. With this new method of analysis, we show dominance of the recent LP-based state-equation heuristic over optimal cost partitioning on single-variable abstractions. We also show that the previously suggested extension of the state-equation heuristic to exploit safe variables cannot lead to an improved heuristic estimate. We experimentally evaluate the potential of the proposed framework on an extensive suite of benchmark tasks.

Comunicació presentada a: the 26th AAAI Conference on Artificial Intelligence, celebrada a Toronto, Canadà, del 22 al 26 de juliol de 2012 It has been shown recently that the complexity of belief tracking in deterministic conformant and contingent planning is exponential in a width parameter that is often bounded and small. In this work, we introduce a new width notion that applies to non-deterministic conformant and contingent problems as well. We also develop a belief tracking algorithm for non-deterministic problems that is exponential in the problem width, analyze the width of non-deterministic benchmarks, compare the new notion to the previous one over deterministic problems, and present experimental results. H. Geffner is partially supported by grants TIN2009-10232, MICINN, Spain, and EC-7PM SpaceBook.

Rewriting is widely used to optimise owl:sameAs reasoning in materialisation based OWL 2 RL systems. We investigate issues related to both the correctness and efficiency of rewriting, and present an algorithm that guarantees correctness, improves efficiency, and can be effectively parallelised. Our evaluation shows that our approach can reduce reasoning times on practical data sets by orders of magnitude., This is the technical report supporting the AAAI 2015 Conference submission with the same title

In order to properly regulate iterated belief revision, Darwiche and Pearl (1997) model belief revision as revising epistemic states by propositions. An epistemic state in their sense consists of a belief set and a set of conditional beliefs. Although the denotation of an epistemic state can be indirectly captured by a total preorder on the set of worlds, it is unclear how to directly capture the structure in terms of the beliefs and conditional beliefs it contains. In this paper, we first provide an axiomatic characterisation for epistemic states by using nine rules about beliefs and conditional beliefs, and then argue that the last two rules are too strong and should be eliminated for characterising the belief state of an agent. We call a structure which satisfies the first seven rules a general epistemic state (GEP). To provide a semantical characterisation of GEPs, we introduce a mathematical structure called belief algebra, which is in essence a certain binary relation defined on the power set of worlds.We then establish a 1-1 correspondence between GEPs and belief algebras, and show that total preorders on worlds are special cases of belief algebras. Furthermore, using the notion of belief algebras, we extend the classical iterated belief revision rules of Darwiche and Pearl to our setting of general epistemic states.

Module extraction - the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S - has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results., Comment: 13 pages. To appear in AAAI-15