Planning Through Stochastic Local Search and Temporal Action Graphs in LPG

We present some techniques for planning in domains specified with the recent standard language PDDL2.1, supporting 'durative actions' and numerical quantities. These techniques are implemented in LPG, a domain-independent planner that took part in the 3rd International Planning Competition (IPC). LPG is an incremental, any time system producing multi-criteria quality plans. The core of the system is based on a stochastic local search method and on a graph-based representation called 'Temporal Action Graphs' (TA-graphs). This paper focuses on temporal planning, introducing TA-graphs and proposing some techniques to guide the search in LPG using this representation. The experimental results of the 3rd IPC, as well as further results presented in this paper, show that our techniques can be very effective. Often LPG outperforms all other fully-automated planners of the 3rd IPC in terms of speed to derive a solution, or quality of the solutions that can be produced

Plan stability: replanning versus plan repair

The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the {em stable} repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning

Reformulation in planning

Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques

Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation

Cerebrospinal fluid analysis for HIV replication and biomarkers of immune activation and neurodegeneration in long-term atazanavir/ritonavir monotherapy treated patients

Background: Cerebrospinal fluid (CSF) viral escape is a concern in ritonavir-boosted protease inhibitors monotherapy. The aim was to assess HIV-RNA, biomarkers of immune activation and neurodegeneration, and atazanavir concentrations in CSF of patients on successful long-term atazanavir/ritonavir (ATV/r) monotherapy. Methods: This is a substudy of the multicentric, randomized, open-label, noninferiority trial monotherapy once a day with atazanavir/ritonavir (NCT01511809), comparing the ongoing ATV/r along with 2 nucleoside retrotranscriptase inhibitors (NRTIs) regimen to a simplified ATV/r monotherapy. Patients with plasma HIV-RNA < 50 copies/mL after at least 96 study weeks were eligible. We assessed HIV-RNA, soluble (s)CD14, sCD163, CCL2, CXCL10, interleukin-6, and YKL40 by enzyme-linked immunosorbent assay; neopterin, tryptophan, kynurenine, and neurofilament by immunoassays; and ATV concentrations by liquid chromatography–mass spectrometry in paired plasma and CSF samples. Variables were compared with Wilcoxon rank-sum or Fisher exact test, as appropriate. Results: HIV-RNA was detected in the CSF of 1/11 patients on ATV/r monotherapy (114 copies/mL), without neurological symptoms, who was successfully reintensified with his previous 2NRTIs, and in none of the 12 patients on ATV/r + 2NRTIs. CSF biomarkers and ATV concentrations did not differ between the 2 arms. Conclusions: CSF escape was uncommon in patients on long-term ATV/r monotherapy and was controlled with reintensification

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Quantized spin waves in the metallic state of magnetoresistive manganites

High resolution spin waves measurements have been carried out in ferromagnetic (F) La(1-x)(Sr,Ca)xMnO3 with x(Sr)=0.15, 0.175, 0.2, 0.3 and x(Ca)=0.3. In all q-directions, close to the zone boundary, the spin wave spectra consist of several energy levels, with the same values in the metallic and the x\approx 1/8 ranges. Mainly the intensity varies, jumping from the lower energy levels determined in the x\approx 1/8 range to the higher energy ones observed in the metallic state. On the basis of a quantitative agreement found for x(Sr)=0.15 in a model of ordered 2D clusters, the spin wave anomalies of the metallic state can be interpreted in terms of quantized spin waves within the same 2D clusters, embedded in a 3D matrix.Comment: 4 pages, 5 figure