Célia da Costa Pereira, Fr�d�rick Garcia, J�r�me Lang et al. · International Journal of Intelligent Systems · 1997
This article proposes a framework for planning under uncertainty given a partially known initial state and a set of actions having nondeterministic (disjunctive) effects, some being more possible (normal) than the others. The problem, henceforth called possibilistic planning problem, is represented in an extension of the STRIPS formalism in which the initial state of the world and the graded nondeterministic effects of actions are described by possibility distributions. Two notions of solution plans are introduced: γ-acceptable plans that lead to a goal state with a certainty greater than a given threshold γ, and optimally safe plans that lead to a goal state with maximal certainty. It is shown that the search of a γ-acceptable plan amounts to solve a derived planning problem that has only pure (nongraded) nondeterministic actions. A sound and complete partial order planning algorithm, called NDP, has been developed for such classical nondeterministic planning problems. The generation of γ-acceptable and optimally safe plans is achieved by two sound and complete planning algorithms: POSPLAN that relies on NDP, and POSPLAN* that can be seen as a hierarchical version of POSPLAN. The possibilistic planning framework is illustrated throughout the article by an example in the agronomic domain. © 1997 John Wiley & Sons, Inc.
Cited by 18
Piergiorgio Bertoli, Ro Cimatti, Paolo Traverso · 2004
Abstract. Methods that interleave planning and execution are a practical solution to deal with complex planning problems in non-deterministic domains under partial observability. However, most of the existing approaches do not tackle in a principled way the impor-tant issue of termination of the planning-execution loop, or only do so considering specific assumptions over the domains. In this paper, we tackle the problem of interleaving planning and execution relying on a general framework, which is able to deal with nondeterministic, partially observable planning domains. We pro-pose a new, general planning algorithm that guarantees the termi-nation of the interleaving of planning and execution: either the goal is achieved, or the system detects that there is no longer a guarantee to progress toward it. Our experimental analysis shows that our algorithm can efficiently solve planning problems that cannot be tackled with a state of the art off-line planner for nondeterministic domains under partial observ-ability, MBP. Moreover, we show that our algorithm can efficiently detect situations where progress toward the goal can be no longer guaranteed. 1
Cited by 0
Cyril Nicaud · 2012
Abstract. Glushkov’s algorithm builds an ε-free nondeterministic automaton from a given regular expression. In the worst case, its number of states is linear and its number of transitions is quadratic in the size of the expression. We show in this paper that in average, the number of transitions is linear.
Cited by 0
Célia da Costa Pereira, Frédérick Garçia, Jérôme Lang et al. · International Journal of Intelligent Systems · 1997
This article proposes a framework for planning under uncertainty given a partially known initial state and a set of actions having nondeterministic (disjunctive) effects, some being more possible (normal) than the others. The problem, henceforth called possibilistic planning problem, is represented in an extension of the STRIPS formalism in which the initial state of the world and the graded nondeterministic effects of actions are described by possibility distributions. Two notions of solution plans are introduced: γ-acceptable plans that lead to a goal state with a certainty greater than a given threshold γ, and optimally safe plans that lead to a goal state with maximal certainty. It is shown that the search of a γ-acceptable plan amounts to solve a derived planning problem that has only pure (nongraded) nondeterministic actions. A sound and complete partial order planning algorithm, called NDP, has been developed for such classical nondeterministic planning problems. The generation of γ-acceptable and optimally safe plans is achieved by two sound and complete planning algorithms: POSPLAN that relies on NDP, and POSPLAN* that can be seen as a hierarchical version of POSPLAN. The possibilistic planning framework is illustrated throughout the article by an example in the agronomic domain. © 1997 John Wiley & Sons, Inc.
Cited by 23
G. Mutyalamma | K. Komali | G. Pushpa · Bulletin of Miscellaneous Information (Royal Gardens Kew)
In automata theory a minimization is the task of transforming a given finite state machine into an equivalent automation that has a minimum number of states. Here, the reduction of Deterministic Finite Automata(DFA) is very simple whereas Nondeterministic Finite Automata(NFA) is complex because which has maximium number of possible paths to reach new states. So a minimal NFA is a primal problem in automata theory. We consider the problem of approximating a minimal NFA or a minimal regular expression. There are several approaches to NFA minimization either without approximation guarantees or running in at least exponential time. Here this paper introducing the new NFA reduction algorithm for the minimization of NFA. This algorithm will reduce number of state transitions of Nondeterministic Finite Automata. NFA reduction algorithm also resolves the complexity of Kameda- Weiner algorithm. This paper shown empirically that this algorithm is effective in largely reducing the memory requirement of NFA minimization algorithm. Reducing the size of NFA by using NFA Reduction Algorithm has been shown to reduce importantly the search time. G. Mutyalamma | K. Komali | G. Pushpa"A Novel Algorithm for Reduction of Non-Deterministic Finite Automata" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd8233.pdf Article URL: http://www.ijtsrd.com/computer-science/other/8233/a-novel-algorithm-for-reduction-of--non-deterministic-finite-automata/g-mutyalamma
Cited by 0
Milan Tuba, Nebojša Bačanin, Adis Alihodžić · 2014
RFID network planning involves many objectives and constraints and it belongs to the class of NP-hard problems. Such problems were recently successfully tackled by nondeterministic optimization metaheuristics where swarm intelligence represents a prominent branch. We present improved firefly algorithm adjusted for multi-objective RFID network planning where our proposed algorithm improved results considering all relevant performance measures tested on the same benchmark functions and compared to the previously known results from the literature.
Cited by 12
Detlef Ronneburger · 2001
The restriction of nondeterminism to unambiguous computations has been used as an attempt to clarify the relationship between deterministic and nondeterministic models of computation. In this paper we will consider three different, increasingly more restrictive notions of unambiguity. While all of them coincide for polynomial time bounded computation, this does not seem to happen for logarithmic space bounded computation. Moreover, the least restrictive class UL seems to coincide with NL (at least under a widely believed assumption) while the more restrictive classes StUL and RUL have a deterministic algorithm that runs in time log2 n log log n.Soin this paper we will give an overview over these classes and their relationship with deterministic and nondeterministic logspace. 1
Cited by 0
Timothy Kam, Tiziano Villa, Robert K. Brayton et al. · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 1997
This paper addresses state minimization problems of different classes of nondeterministic finite-state machines (NDFSMs). We describe a fully implicit algorithm for state minimization of pseudo nondeterministic FSM's (PNDFSMs). The results of our implementation are reported and shown to be superior to a previous explicit formulation. We could solve exactly all but one problem of a published benchmark, while an explicit program could complete approximately one half of the examples, and in those cases, with longer run times. Then we present a theoretical solution to the problem of exact state minimization of general NDFSMs, based on the proposal of generalized compatibles. This gives an algorithmic framework to explore behaviors contained in a general NDFSM.
Cited by 11
Brian DuSell · arXiv (Cornell University) · 2023
Human language is full of compositional syntactic structures, and although neural networks have contributed to groundbreaking improvements in computer systems that process language, widely-used neural network architectures still exhibit limitations in their ability to process syntax. To address this issue, prior work has proposed adding stack data structures to neural networks, drawing inspiration from theoretical connections between syntax and stacks. However, these methods employ deterministic stacks that are designed to track one parse at a time, whereas syntactic ambiguity, which requires a nondeterministic stack to parse, is extremely common in language. In this dissertation, we remedy this discrepancy by proposing a method of incorporating nondeterministic stacks into neural networks. We develop a differentiable data structure that efficiently simulates a nondeterministic pushdown automaton, representing an exponential number of computations with a dynamic programming algorithm. We incorporate this module into two predominant architectures: recurrent neural networks (RNNs) and transformers. We show that this raises their formal recognition power to arbitrary context-free languages, and also aids training, even on deterministic context-free languages. Empirically, neural networks with nondeterministic stacks learn context-free languages much more effectively than prior stack-augmented models, including a language with theoretically maximal parsing difficulty. We also show that an RNN augmented with a nondeterministic stack is capable of surprisingly powerful behavior, such as learning cross-serial dependencies, a well-known non-context-free pattern. We demonstrate improvements on natural language modeling and provide analysis on a syntactic generalization benchmark. This work represents an important step toward building systems that learn to use syntax in more human-like fashion.
Cited by 0Open access
Matti Nykänen · 2000
We wish to use a given nondeterministic two-way multi-tape acceptor as a transducer by supplying the contents for only some of its input tapes, and asking it to generate the missing contents for the other tapes. We provide here an algorithm for assuring beforehand that this transduction always results in a finite set of answers. We also develop an algorithm for evaluating these answers whenever the previous algorithm indicated their finiteness. Furthermore, our algorithms can be used for speeding up the simulation of these acceptors even when not used as transducers.
Cited by 0
Wojciech Czerwiński, Petr Jančar · arXiv (Cornell University) · 2014
Branching bisimilarity on normed BPA processes was recently shown to be decidable by Yuxi Fu (ICALP 2013) but his proof has not provided any upper complexity bound. We present a simpler approach based on relative prime decompositions that leads to a nondeterministic exponential-time algorithm; this is close to the known exponential-time lower bound.
Cited by 0Open access
Jianrong Wang, X. Feng, Tianyi Xu et al. · IEEE Internet of Things Journal · 2020
Smart vehicles can cooperate in teams to perform crowdsensing tasks in smart cities. A critical challenge in this regard is to build a secure model for nondeterministic vehicle teams to achieve maximum social welfare. Although several crowdsensing models have been proposed, none of them has focused on real-time vehicle teamwork. In this article, to the best of our knowledge, we propose the first secure model, called blockchain-based nondeterministic teamwork cooperation (BNTC), for nondeterministic teamwork cooperation in a vehicular crowdsensing system. We model the system as a multiconditional NP-complete problem by explicitly considering the dynamic features of task issuers and workers. To solve the problem, we propose the winning teams selected (WTS) algorithm based on a reverse auction and utilize a knapsack-based method to solve the models. We consider the credit of teams for determining the payment. Thus, we propose a credit-based team payment (CTP) algorithm for BNTC to maximize the welfare of the system. We also propose a general blockchain-based framework to address trust issues and security challenges to make the method suitable for use in practical applications. Based on theoretical analyses and extensive simulations, we demonstrate that the proposed model performs better than the baselines and can achieve the maximum social welfare. Implementation with Ethereum suggests our model can operate within a reasonable cost.
Cited by 26
Natsuki Urabe, Ichiro Hasuo · Logical Methods in Computer Science · 2017
Notions of simulation, among other uses, provide a computationally tractable and sound (but not necessarily complete) proof method for language inclusion. They have been comprehensively studied by Lynch and Vaandrager for nondeterministic and timed systems; for B\"{u}chi automata the notion of fair simulation has been introduced by Henzinger, Kupferman and Rajamani. We contribute to a generalization of fair simulation in two different directions: one for nondeterministic tree automata previously studied by Bomhard; and the other for probabilistic word automata with finite state spaces, both under the B\"{u}chi acceptance condition. The former nondeterministic definition is formulated in terms of systems of fixed-point equations, hence is readily translated to parity games and is then amenable to Jurdzi\'{n}ski's algorithm; the latter probabilistic definition bears a strong ranking-function flavor. These two different-looking definitions are derived from one source, namely our coalgebraic modeling of B\"{u}chi automata. Based on these coalgebraic observations, we also prove their soundness: a simulation indeed witnesses language inclusion.
Cited by 0Open access
Natsuki Urabe, Ichiro Hasuo · SHILAP Revista de lepidopterología · 2017
Notions of simulation, among other uses, provide a computationally tractable and sound (but not necessarily complete) proof method for language inclusion. They have been comprehensively studied by Lynch and Vaandrager for nondeterministic and timed systems; for B\"{u}chi automata the notion of fair simulation has been introduced by Henzinger, Kupferman and Rajamani. We contribute to a generalization of fair simulation in two different directions: one for nondeterministic tree automata previously studied by Bomhard; and the other for probabilistic word automata with finite state spaces, both under the B\"{u}chi acceptance condition. The former nondeterministic definition is formulated in terms of systems of fixed-point equations, hence is readily translated to parity games and is then amenable to Jurdzi\'{n}ski's algorithm; the latter probabilistic definition bears a strong ranking-function flavor. These two different-looking definitions are derived from one source, namely our coalgebraic modeling of B\"{u}chi automata. Based on these coalgebraic observations, we also prove their soundness: a simulation indeed witnesses language inclusion.
Cited by 2Open access
Alexandra Coman, Héctor Muñoz‐Avila · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · 2013
We study the problem of generating a set of Finite State Machines (FSMs) modeling the behavior of multiple, distinct NPCs. We observe that nondeterministic planning techniques can be used to generate FSMs by following conventions typically used when manually creating FSMs modeling NPC behavior. We implement our ideas in DivNDP, the first algorithm for automated diverse FSM generation.
Cited by 4Open access
Kimmo Fredriksson · 2000
Given a text string T of lenght n and a pattern string P of lenght m over some alphabet Σ, we want to find the occurrences of P ′ from T such that P ′ can be derived from P by set of local swaps, i.e. transpositions of two adjacent characters, each character swapping at most once. We give several simple but fast algorithms for the problem. The first algorithm is based on Boyer–Moore–Horspool approach. The second algorithm uses a nondeterministic finite automaton that is simulated using a shift–or type method. We improve the shift–or to take only time O(n / log |Σ | s⌈(m + 1)/w⌉), where s ≥ |Σ | is the space usage of the algorithm, and w is the lenght of the machine word. This algorithm is sublinear for small patterns and alphabets, and is asymptotically the fastest bit–parallel simulation of sufficiently simple nondeterministic finite automata. Finally, we show how bit–parallel suffix automaton can be used to solve the problem in optimal average time O(n log m/m), while being only O(n⌈m/w⌉) in the worst case. The algorithms are very simple to implement, and experimental results show that they are very fast on natural language.
Cited by 0
Mikhail Abramyan · University proceedings Volga region Physical and mathematical sciences · 2021
Background. The study considers various aspects of constructing iterative anytime algorithms for solving the problem of state minimization of nondeterministic finite automata. Although this problem was posed back in the 60s of the 20th century, it is NP-hard; therefore, the development of efficient algorithms for its solution remains a relevant problem. The purpose of the study is to analysis of the choice influence of different variants of subproblems’ weight characteristics using branch and bound method on the efficiency of the basic algorithm version and its modifications, using various heuristics. Materials and methods. The study is based on the analysis of numerical experiments performed using the software implementation of the described algorithms. The program is implemented in C# 6.0 for the .NET Framework. Results. The results are the revealed patterns associated with the choice of the weight characteristics of subtasks in the branch and bound method for the algorithm of the nondeterministic finite automata state minimization. Conclusions. We determine optimal weight characteristics both for the basic algorithm and for its modifications supplied with additional heuristics. We also show that the use of these weight characteristics together with additional heuristics can significantly increase efficiency of the developed algorithms.
Cited by 4Open access
Chang Hu, Mingjun Xiao, Liusheng Huang et al. · 2016
Nowadays, vehicles have shown great potential in crowdsensing. To guarantee a good Quality of Service (QoS), stimulating enough vehicles to participate in crowdsensing is very necessary. In this paper, we focus on the incentive mechanism design in the vehicle-based nondeterministic crowdsensing. Different from existing works, we take into consideration that each vehicle performs sensing tasks along some trajectories with different probabilities, and each task must be successfully performed with a joint probability no less than a threshold. Designing an incentive mechanism for such a nondeterministic crowdsensing system is challenging, which contains a non-trivial set cover problem with non-linear constraints. To solve the problem, we propose a truthful incentive mechanism based on reverse auction, including an approximation algorithm to select winning bids with a nearly minimum social cost, and a payment algorithm to determine the payments for all participants. Through theoretical analysis, we prove that our incentive mechanism is truthful and individual rational, and we give an approximation ratio of the winning bid selection algorithm. In addition, we conduct extensive simulations, based on a real vehicle trace, to validate the performances of the proposed incentive mechanism.
Cited by 18
Silvia Cattani, Roberto Segala · 2002
We propose decision algorithms for bisimulation relations de ned on probabilistic automata, a model for concurrent nondeterministic systems with randomization. The algorithms decide both strong and weak bisimulation relations based on deterministic as well as randomized schedulers. These algorithms extend and complete other known algorithms for simpler relations and models. The algorithm we present for strong probabilistic bisimulation has polynomial time complexity, while the algorithm for weak probabilistic bisimulation is exponential; however we argue that the latter is feasible in practice.
Cited by 0
Izzatdin Abdul Aziz, Low Tan Jung, Mazlina Mehat · 2009
Abstract—The Traveling Salesman Problem (TSP) is a well known Nondeterministic Polynomial (NP) problem. A number of sequential algorithms have been well defined to solve TSP. However in this paper the authors demonstrate an alternative way of solving TSP with parallelism by modifying Prim’s algorithm and integrate the modified algorithm with a Random algorithm making the whole process more computational extensive in visiting all the nodes/cities. This paper discussed on converting the sequential algorithm into a parallel version to be computed in a High Performance Computer (HPC) using Message Passing Interface (MPI) libraries running on ROCKS open source systems. Outcome of load balancing and cluster performance is also discussed.
Cited by 1