Sampling-based algorithms are a common approach to high-dimensional real-world path planning problems. Unfortunately the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints. This paper formulates the practicality of paths based on the notion of soft constraints found in the Planning Domain Definition Language 3 (PDDL3) (Gerevini and Long, 2005) and a range of optimization strategies are developed targeted towards user-preferred qualities by integrating soft constraints in the pre-processing, planning and post-processing phases of the sampling-based path planners. An auction-based resource allocation approach coordinates competing optimization strategies. This approach uses an adaptive bidding strategy for each optimizer and in each round the optimizer with the best predicted performance is selected. This general coordination system allows for flexibility in both the number and types of the optimizers used. Experimental validation demonstrates the effectiveness of the approach.

The fundamental problem in robotic exploration and mapping of an unknown environment is answering the question 'have I been here before?', which involves disambiguating the robot's current location from previously visited or known locations. One approach to answering this problem in embedded topological worlds is to resort to the use of an external aid that can help the robot disambiguate places. Here we investigate the power of different marker-based aids in exploring undirected topological graphs. We demonstrate that for undirected graphs, certain marker aids are insufficient, while others have powers that are sufficient to develop asymptotically optimal exploration algorithms.

Literature and folklore is rife with a range of oracles that have been used by explorers to explore unknown environments. But how effective are these various oracles? This paper considers the power of string and string-like oracles to map an unknown embedded topological environment. We demonstrate that for undirected graphs, even very short strings can be used to explore an unknown environment but that significant performance improvements can be found when longer strings are available.

The well-known Transport Control Protocol (TCP) is a crucial component of the TCP/IP architecture on which the Internet is built, and is a de facto standard for reliable communication on the Internet. At the heart of the TCP protocol is its congestion control algorithm. While most practitioners believe that TCP congestion control algorithm performs very well, a complete analysis of the congestion control algorithm is yet to be done. A lot of effort has, therefore, gone into the evaluation of different performance metrics like throughput and average latency under TCP. In this paper, we approach the problem from a different perspective and use the the competitive analysis framework to provide some answers to the question “how good is the TCP/IP congestion control algorithm? ” First, we prove that for networks with a single bottleneck (or point of congestion), TCP is competitive to the optimal centralized (global) algorithm in minimizing the user-perceived latency or flow time of the sessions, provided we limit the adversary by giving it strictly less resources than TCP. Specifically, we show that with O(1) times as much bandwidth and O(1) extra time per job, TCP is O(1)-competitive against an optimal global algorithm. We motivate the need for allowing TCP to have extra resources by observing that existing lower bounds for nonclairvoyant scheduling algorithms imply that no online, distributed, non-clairvoyant algorithm can be competitive with an optimal offline algorithm if both algorithms were given the same resources. Second, we show that TCP is fair by proving that it converges quickly to allocations where every session gets its fair share of network bandwidth. 1

We identify and study a natural and frequently occurring subclass of Concurrent Read, Exclusive Write Parallel Random Access Machines (CREW-PRAMs). Called Concurrent Read, Owner Write, or CROW-PRAMS, these are machines in which each global memory location is assigned a unique “owner” processor, which is the only processor allowed to write into it. Considering the difficulties that would be involved in physically realizinga full CREW-PRAM model and demonstrate its stability under several definitional changes. Second, we precisely characterize the power of the CROW-PRAM by showing that the class of languages recognizable by it in time O (log n) (and implicity with a polynomial number of processors) is exactly the class LOGDCFL of languages log space reducible to deterministic context-free languages. Third, using the same basic machinery, we show that the recognition problem for deterministic context-free languages can be solved quickly on a deterministic auxilliary pushdown automation having random access to its input tape, a log n space work tape, and pushdown store of small maximum height. For example, time O ( n 1 + ε ) is achievable with pushdown height O (log 2 n ). These result extend and unify work of von Braunmöhl, Cook, Mehlhorn, and Verbeek, Klein and Reif; and Rytter.

We show that there is a good algorithm for scheduling the average completion time of a set of unknown DAGs (i.e., data dependency relation graphs of programs) on a multiprocessor in the PRAM model [12] (or other similar shared memory models.) Then, we show that a large class of parallel jobs can be scheduled with near-optimal average completion time in the BSP model [31] though this is not possible for the class of all unknown DAGs [6] (the same holds for other similar distributed memory models.) 1 Introduction The execution of programs on a uniprocessor system is straightforward: any non-idle scheduling strategy achieves optimal schedule length. If average completion time is taken as the system performance metric, the Round-Robin policy achieves a mean response time within twice the optimum [20]. Notice that the Round-Robin policy does not need to know the control/data dependency graph (DAG) of the program's execution at compile time. On the other hand, scheduling of multiprocessor s...

We present a randomized parallel algorithm for constructing the 3D convex hull on a generic p-processor coarse grained multicomputer with arbitrary interconnection network and n/p local memory per pro-Permission to make.digitirl/llarci copies of :111or p:~rt of [his nl:llcri:ll wiLhout fee is granted provided lhat the ct]pics ;Ire II(J1 m:ldc {Jr dis~l-il,~itcd for profit or commercial advantage, the ACM copyrighl/sccvcr notice, the title of the publication and its date appear, and notice is given that copyright is by

Following the recent independent proofs of Immerman [SIAM J. Comput., 17 (1988), pp. 935–938] and Szelepcsenyi [Bull. European Assoc. Theoret. Comput. Sci., 33 (1987), pp. 96–100] that nondeterministic space-bounded complexity classes are closed under complementation, two further applications of the inductive counting technique are developed. First, an errorless probabilistic algorithm for the undirected graph s-t connectivity problem that runs in $O(\log n)$ space and polynomial expected time is given. Then it is shown that the class LOGCFL is closed under complementation. The latter is a special case of a general result that shows closure under complementation of classes defined by semi-unbounded fan-in circuits (or, equivalently, nondeterministic auxiliary pushdown automata or tree-size bounded alternating Turing machines). As one consequence, it is shown that small numbers of “role switches” in two-person pebbling can be eliminated.

Previous article Full AccessErratum: Two Applications of Indctive Counting for Complementation ProblemsAllan Borodin, Stephen A. Cook, Patrick W. Dymond, Walter L. Ruzzo, and Martin TompaAllan Borodin, Stephen A. Cook, Patrick W. Dymond, Walter L. Ruzzo, and Martin Tompahttps://doi.org/10.1137/0218084PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout"Erratum: Two Applications of Indctive Counting for Complementation Problems." SIAM Journal on Computing, 18(6), p. 1283[1] Allan Borodin, , Stephen A. Cook, , Patrick W. Dymond, , Walter L. Ruzzo and , Martin Tompa, Two applications of inductive counting for complementation problems, SIAM J. Comput., 18 (1989), 559–578 10.1137/0218038 90k:68049a 0678.68031 LinkISIGoogle Scholar[2] John Gill, Computational complexity of probabilistic Turing machines, SIAM J. Comput., 6 (1977), 675–695 10.1137/0206049 57:4616 0366.02024 LinkISIGoogle Scholar[3] Hermann Jung, On probabilistic time and spaceAutomata, languages and programming (Nafplion, 1985), Lecture Notes in Comput. Sci., Vol. 194, Springer, Berlin, 1985, 310–317 87b:68039 0599.68043 CrossrefGoogle Scholar Previous article FiguresRelatedReferencesCited byDetails Dual VP Classes23 September 2016 | computational complexity, Vol. 26, No. 3 Cross Ref Input Reversals and Iterated Pushdown Automata: A New Characterization of Khabbaz Geometric Hierarchy of Languages Cross Ref Computational Complexity Cross Ref Trading Space for Time in Undirected s-t ConnectivityAndrei Z. Broder, Anna R. Karlin, Prabhakar Raghavan, and Eli Upfal31 July 2006 | SIAM Journal on Computing, Vol. 23, No. 2AbstractPDF (1266 KB)My favorite ten complexity theorems of the past decade1 June 2005 Cross Ref Lower bounds on the length of universal traversal sequencesJournal of Computer and System Sciences, Vol. 45, No. 2 Cross Ref A very hard log space counting class Cross Ref Volume 18, Issue 6| 1989SIAM Journal on Computing History Submitted:03 August 1989Accepted:30 August 1989Published online:13 July 2006 InformationCopyright © 1989 Society for Industrial and Applied MathematicsPDF Download Article & Publication DataArticle DOI:10.1137/0218084Article page range:pp. 1283-1283ISSN (print):0097-5397ISSN (online):1095-7111Publisher:Society for Industrial and Applied Mathematics

This paper presents the new speedups DTIME(T) @@@@ ATIME(T/log T) and DTIME(T) @@@@ PRAM-Time(@@@@T). These improve the results of Hopcroft, Paul, and Valiant that DTIME(T) @@@@ DSPACE(T/log T), and of Paul and Reischuk that DTIME(T) @@@@ ATIME(T log log T/log T). The new approach unifies not only these two previous results, but also the result of Paterson and Valiant that Size(T) @@@@ Depth(O(T/log T)).