121 research outputs found
Symbolic and analytic techniques for resource analysis of Java bytecode
Recent work in resource analysis has translated the idea of amortised resource analysis to imperative languages using a program logic that allows mixing of assertions about heap shapes, in the tradition of separation logic, and assertions about consumable resources. Separately, polyhedral methods have been used to calculate bounds on numbers of iterations in loop-based programs. We are attempting to combine these ideas to deal with Java programs involving both data structures and loops, focusing on the bytecode level rather than on source code
Provably Safe Robot Navigation with Obstacle Uncertainty
As drones and autonomous cars become more widespread it is becoming
increasingly important that robots can operate safely under realistic
conditions. The noisy information fed into real systems means that robots must
use estimates of the environment to plan navigation. Efficiently guaranteeing
that the resulting motion plans are safe under these circumstances has proved
difficult. We examine how to guarantee that a trajectory or policy is safe with
only imperfect observations of the environment. We examine the implications of
various mathematical formalisms of safety and arrive at a mathematical notion
of safety of a long-term execution, even when conditioned on observational
information. We present efficient algorithms that can prove that trajectories
or policies are safe with much tighter bounds than in previous work. Notably,
the complexity of the environment does not affect our methods ability to
evaluate if a trajectory or policy is safe. We then use these safety checking
methods to design a safe variant of the RRT planning algorithm.Comment: RSS 201
Inquisitive Pattern Recognition
The Department of Defense and the Department of the Air Force have funded automatic target recognition for several decades with varied success. The foundation of automatic target recognition is based upon pattern recognition. In this work, we present new pattern recognition concepts specifically in the area of classification and propose new techniques that will allow one to determine when a classifier is being arrogant. Clearly arrogance in classification is an undesirable attribute. A human is being arrogant when their expressed conviction in a decision overstates their actual experience in making similar decisions. Likewise given an input feature vector, we say a classifier is arrogant in its classification if its veracity is high yet its experience is low. Conversely a classifier is non-arrogant in its classification if there is a reasonable balance between its veracity and its experience. We quantify this balance and we discuss new techniques that will detect arrogance in a classifier. Inquisitiveness is in many ways the opposite of arrogance. In nature inquisitiveness is an eagerness for knowledge characterized by the drive to question to seek a deeper understanding and to challenge assumptions. The human capacity to doubt present beliefs allows us to acquire new experiences and to learn from our mistakes. Within the discrete world of computers, inquisitive pattern recognition is the constructive investigation and exploitation of conflict in information. This research defines inquisitiveness within the context of self-supervised machine learning and introduces mathematical theory and computational methods for quantifying incompleteness that is for isolating unstable, nonrepresentational regions in present information models
On the Complexity of Core, Kernel, and Bargaining Set
Coalitional games are mathematical models suited to analyze scenarios where
players can collaborate by forming coalitions in order to obtain higher worths
than by acting in isolation. A fundamental problem for coalitional games is to
single out the most desirable outcomes in terms of appropriate notions of worth
distributions, which are usually called solution concepts. Motivated by the
fact that decisions taken by realistic players cannot involve unbounded
resources, recent computer science literature reconsidered the definition of
such concepts by advocating the relevance of assessing the amount of resources
needed for their computation in terms of their computational complexity. By
following this avenue of research, the paper provides a complete picture of the
complexity issues arising with three prominent solution concepts for
coalitional games with transferable utility, namely, the core, the kernel, and
the bargaining set, whenever the game worth-function is represented in some
reasonable compact form (otherwise, if the worths of all coalitions are
explicitly listed, the input sizes are so large that complexity problems
are---artificially---trivial). The starting investigation point is the setting
of graph games, about which various open questions were stated in the
literature. The paper gives an answer to these questions, and in addition
provides new insights on the setting, by characterizing the computational
complexity of the three concepts in some relevant generalizations and
specializations.Comment: 30 pages, 6 figure
Towards spatial reasoning on building information models
The paper presents a conceptual study on the application of spatial reasoning on building information models. In many cases, building regulations and client demands imply traints on the building design with inherent spatial semantics. If we are able to represent these spatial constraints in a computerinterpretable way, the building design can be checked for fulfilling them. In this context, spatial reasoning technology can be applied in two different ways. First, we can check the consistency of the spatial constraints in effect, i.e. find out whether there are contradictions between them. Second, we can check whether a concrete building design is compliant with these constraints. The paper gives a detailed overview on the currently available spatial calculi and introduces two possible implementation pproaches
07212 Abstracts Collection -- Constraint Databases, Geometric Elimination ang Geographic Information Systems
From 20.05. to 25.05., the Dagstuhl Seminar 07212 ``Constraint Databases, Geometric Elimination and Geographic Information Systems\u27\u27 was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
IST Austria Thesis
Hybrid automata combine finite automata and dynamical systems, and model the interaction of digital with physical systems. Formal analysis that can guarantee the safety of all behaviors or rigorously witness failures, while unsolvable in general, has been tackled algorithmically using, e.g., abstraction, bounded model-checking, assisted theorem proving.
Nevertheless, very few methods have addressed the time-unbounded reachability analysis of hybrid automata and, for current sound and automatic tools, scalability remains critical. We develop methods for the polyhedral abstraction of hybrid automata, which construct coarse overapproximations and tightens them incrementally, in a CEGAR fashion. We use template polyhedra, i.e., polyhedra whose facets are normal to a given set of directions.
While, previously, directions were given by the user, we introduce (1) the first method
for computing template directions from spurious counterexamples, so as to generalize and
eliminate them. The method applies naturally to convex hybrid automata, i.e., hybrid
automata with (possibly non-linear) convex constraints on derivatives only, while for linear
ODE requires further abstraction. Specifically, we introduce (2) the conic abstractions,
which, partitioning the state space into appropriate (possibly non-uniform) cones, divide
curvy trajectories into relatively straight sections, suitable for polyhedral abstractions.
Finally, we introduce (3) space-time interpolation, which, combining interval arithmetic
and template refinement, computes appropriate (possibly non-uniform) time partitioning
and template directions along spurious trajectories, so as to eliminate them.
We obtain sound and automatic methods for the reachability analysis over dense
and unbounded time of convex hybrid automata and hybrid automata with linear ODE.
We build prototype tools and compare—favorably—our methods against the respective
state-of-the-art tools, on several benchmarks
Mathematically provable correct implementation of integrated 2D and 3D representations
representation
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