Safety-Aware Apprenticeship Learning

Apprenticeship learning (AL) is a kind of Learning from Demonstration techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations. In this paper, we study the problem of how to make AL algorithms inherently safe while still meeting its learning objective. We consider a setting where the unknown reward function is assumed to be a linear combination of a set of state features, and the safety property is specified in Probabilistic Computation Tree Logic (PCTL). By embedding probabilistic model checking inside AL, we propose a novel counterexample-guided approach that can ensure safety while retaining performance of the learnt policy. We demonstrate the effectiveness of our approach on several challenging AL scenarios where safety is essential.Comment: Accepted by International Conference on Computer Aided Verification (CAV) 201

Partial replay of long-running applications

Bugs in deployed software can be extremely difficult to track down. Invasive logging techniques, such as logging all non-deterministic inputs, can incur substantial runtime overheads. This paper shows how symbolic analysis can be used to re-create path equivalent executions for very long running programs such as databases and web servers. The goal is to help developers debug such long-running programs by allowing them to walk through an execution of the last few requests or transactions leading up to an error. The challenge is to provide this functionality without the high runtime overheads associated with traditional replay techniques based on input logging or memory snapshots. Our approach achieves this by recording a small amount of information about program execution, such as the direction of branches taken, and then using symbolic analysis to reconstruct the execution of the last few inputs processed by the application, as well as the state of memory before these inputs were executed. We implemented our technique in a new tool called bbr. In this paper, we show that it can be used to replay bugs in long-running single-threaded programs starting from the middle of an execution. We show that bbr incurs low recording overhead (avg. of 10%) during program execution, which is much less than existing replay schemes. We also show that it can reproduce real bugs from web servers, database systems, and other common utilities

SAT-Based Synthesis Methods for Safety Specs

Automatic synthesis of hardware components from declarative specifications is an ambitious endeavor in computer aided design. Existing synthesis algorithms are often implemented with Binary Decision Diagrams (BDDs), inheriting their scalability limitations. Instead of BDDs, we propose several new methods to synthesize finite-state systems from safety specifications using decision procedures for the satisfiability of quantified and unquantified Boolean formulas (SAT-, QBF- and EPR-solvers). The presented approaches are based on computational learning, templates, or reduction to first-order logic. We also present an efficient parallelization, and optimizations to utilize reachability information and incremental solving. Finally, we compare all methods in an extensive case study. Our new methods outperform BDDs and other existing work on some classes of benchmarks, and our parallelization achieves a super-linear speedup. This is an extended version of [5], featuring an additional appendix.Comment: Extended version of a paper at VMCAI'1

Temporal Stream Logic: Synthesis beyond the Bools

Reactive systems that operate in environments with complex data, such as mobile apps or embedded controllers with many sensors, are difficult to synthesize. Synthesis tools usually fail for such systems because the state space resulting from the discretization of the data is too large. We introduce TSL, a new temporal logic that separates control and data. We provide a CEGAR-based synthesis approach for the construction of implementations that are guaranteed to satisfy a TSL specification for all possible instantiations of the data processing functions. TSL provides an attractive trade-off for synthesis. On the one hand, synthesis from TSL, unlike synthesis from standard temporal logics, is undecidable in general. On the other hand, however, synthesis from TSL is scalable, because it is independent of the complexity of the handled data. Among other benchmarks, we have successfully synthesized a music player Android app and a controller for an autonomous vehicle in the Open Race Car Simulator (TORCS.

Program synthesis from polymorphic refinement types

We present a method for synthesizing recursive functions that provably satisfy a given specification in the form of a polymorphic refinement type. We observe that such specifications are particularly suitable for program synthesis for two reasons. First, they offer a unique combination of expressive power and decidability, which enables automatic verification—and hence synthesis—of nontrivial programs. Second, a type-based specification for a program can often be effectively decomposed into independent specifications for its components, causing the synthesizer to consider fewer component combinations and leading to a combinatorial reduction in the size of the search space. At the core of our synthesis procedure is a newalgorithm for refinement type checking, which supports specification decomposition. We have evaluated our prototype implementation on a large set of synthesis problems and found that it exceeds the state of the art in terms of both scalability and usability. The tool was able to synthesize more complex programs than those reported in prior work (several sorting algorithms and operations on balanced search trees), as well as most of the benchmarks tackled by existing synthesizers, often starting from a more concise and intuitive user input.National Science Foundation (U.S.) (Grant CCF-1438969)National Science Foundation (U.S.) (Grant CCF-1139056)United States. Defense Advanced Research Projects Agency (Grant FA8750-14-2-0242

Abstract Learning Frameworks for Synthesis

We develop abstract learning frameworks (ALFs) for synthesis that embody the principles of CEGIS (counter-example based inductive synthesis) strategies that have become widely applicable in recent years. Our framework defines a general abstract framework of iterative learning, based on a hypothesis space that captures the synthesized objects, a sample space that forms the space on which induction is performed, and a concept space that abstractly defines the semantics of the learning process. We show that a variety of synthesis algorithms in current literature can be embedded in this general framework. While studying these embeddings, we also generalize some of the synthesis problems these instances are of, resulting in new ways of looking at synthesis problems using learning. We also investigate convergence issues for the general framework, and exhibit three recipes for convergence in finite time. The first two recipes generalize current techniques for convergence used by existing synthesis engines. The third technique is a more involved technique of which we know of no existing instantiation, and we instantiate it to concrete synthesis problems

Methodology for optimization of distributed biomass resources evaluation, management and final energy use

[EN] A methodology has been developed to assess optimal management and energy use of distributed biomass resources, where logistics is a main factor but other features must be also considered: biomass resources properties (quantity, quality, seasonality & availability), plant size effect, available technologies for power, heat and solid biofuels generation, CO2 emissions balance and quantification of potential biofuel consumers. This methodology provides a quantification and characterization of biomass resources, a list of optimal locations from logistic point of view and the necessary data to perform detailed technical, economic and environmental analysis of the different biomass energy use options. It has been applied to three districts of the Valencian region in Spain and main results and conclusions are also included in this paper.This work was completed in the framework of the activities of the biomass research group of the IIE (Instituto de Ingenieria Energetica) in regional, national and international. The authors deeply thank all the organizations involved in these projects for their support and, specially, IMPIVA-Generalitat Valenciana, the Spanish Ministry of Science and Technology and the European Commission for the projects BIOVAL, BIODER and EU-DEEP respectively, for the provided funding that made this work possible.Alfonso-Solar, D.; Perpiñá, C.; Pérez-Navarro, A.; Peñalvo-López, E.; Vargas-Salgado, C.; Cárdenas, R. (2009). Methodology for optimization of distributed biomass resources evaluation, management and final energy use. Biomass and Bioenergy. 33(8):1070-1079. https://doi.org/10.1016/j.biombioe.2009.04.0021070107933