Symbolic performance analysis of elastic systems

Elastic systems, either synchronous or asynchronous, can be optimized for the average-case performance when they have units with early evaluation or variable latency. The performance evaluation of such systems using analytical methods is a complex problem and may become a bottleneck when an extensive exploration of different architectural configurations must be done. This paper proposes an analytical method for performance evaluation using symbolic expressions. Two version of the method are presented: an exact method that has high run time complexity and an efficient approximate method that computes the lower bound of the system throughput.Peer ReviewedPostprint (published version

Answer Set Programming based on Propositional Satisfiability

Answer set programming (ASP) emerged in the late 1990s as a new logic programming paradigm that has been successfully applied in various application domains. Also motivated by the availability of efficient solvers for propositional satisfiability (SAT), various reductions from logic programs to SAT were introduced. All these reductions, however, are limited to a subclass of logic programs or introduce new variables or may produce exponentially bigger propositional formulas. In this paper, we present a SAT-based procedure, called ASPSAT, that (1) deals with any (nondisjunctive) logic program, (2) works on a propositional formula without additional variables (except for those possibly introduced by the clause form transformation), and (3) is guaranteed to work in polynomial space. From a theoretical perspective, we prove soundness and completeness of ASPSAT. From a practical perspective, we have (1) implemented ASPSAT in Cmodels, (2) extended the basic procedures in order to incorporate the most popular SAT reasoning strategies, and (3) conducted an extensive comparative analysis involving other state-of-the-art answer set solvers. The experimental analysis shows that our solver is competitive with the other solvers we considered and that the reasoning strategies that work best on ‘small but hard’ problems are ineffective on ‘big but easy’ problems and vice versa

Проблема проверки выполнимости формул разрешимых теорий (обзор)

Данная работа посвящена анализу современного состояния исследований проблемы проверки выполнимости формул разрешимых теорий 1-го порядка на основе ѕленивого подходаї, т.е. на интеграции SAT-решателей с T -решателями. Охарактеризована структура SAT-решателя, построенного на основе управляющей конфликтами DPLL-процедуре. Рассмотрены основные понятия и принципы, используемые в процессе построения современных T -решателей. Изложение иллюстрируется на примере решателя, предназначенного для анализа выполнимости формул линейной целочисленной арифметики. Охарактеризованы методы организации взаимодействия SAT-решателей и T -решателей.Дану статтю присв’ячено аналiзу сучасного стану дослiджень проблеми перевiрки здiйсненостi формул теорiй 1-го порядку на основi ѕледащого пiдходуї, тобто на iнтеграцiї SAT-вирiшувачiв з T -вирiшувачами. Охарактеризовано структуру SAT-вирiшувача, який побудовано на основi керуючою конфлiктами DPLL-процедури. Розглянуто основнi поняття та принципи, якi використуються при побудовi сучасних T -вирiшувачiв. Викладення iлюструється на прикладi вирiшувача, який призначено для перевiрки здiйсненостi формул лiнiйної арифметики цiлих чисел. Охарактеризовано методи iнтеграцiї SAT-вирiшувачiв з T -вирiшувачами.Given paper is devoted to analysis of the state of the art for investigations of the problem of checking for satisfiability of formulae in decidable first-order theories on the base of the lazy approach, i.e. on integration of SAT-solvers with T -solvers. The structure of SAT-solver designed on the base of conflict driven DPLL procedure is characterized. Basic notions and principles applied in the process of elaboration of modern T -solvers are considered. They are presented in detail for example of a solver intended for checking of satisfiability for formulae of linear integer arithmetic. Methods of integration of SAT-solvers with T -solvers are characterized

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

Modelling and solving temporal reasoning as propositional satisfiability

AbstractRepresenting and reasoning about time dependent information is a key research issue in many areas of computer science and artificial intelligence. One of the best known and widely used formalisms for representing interval-based qualitative temporal information is Allen's interval algebra (IA). The fundamental reasoning task in IA is to find a scenario that is consistent with the given information. This problem is in general NP-complete.In this paper, we investigate how an interval-based representation, or IA network, can be encoded into a propositional formula of Boolean variables and/or predicates in decidable theories. Our task is to discover whether satisfying such a formula can be more efficient than finding a consistent scenario for the original problem. There are two basic approaches to modelling an IA network: one represents the relations between intervals as variables and the other represents the end-points of each interval as variables. By combining these two approaches with three different Boolean satisfiability (SAT) encoding schemes, we produced six encoding schemes for converting IA to SAT. In addition, we also showed how IA networks can be formulated into satisfiability modulo theories (SMT) formulae based on the quantifier-free integer difference logic (QF-IDL). These encodings were empirically studied using randomly generated IA problems of sizes ranging from 20 to 100 nodes. A general conclusion we draw from these experimental results is that encoding IA into SAT produces better results than existing approaches. More specifically, we show that the new point-based 1-D support SAT encoding of IA produces consistently better results than the other alternatives considered. In comparison with the six different SAT encodings, the SMT encoding came fourth after the point-based and interval-based 1-D support schemes and the point-based direct scheme. Further, we observe that the phase transition region maps directly from the IA encoding to each SAT or SMT encoding, but, surprisingly, the location of the hard region varies according to the encoding scheme. Our results also show a fixed performance ranking order over the various encoding schemes

CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning

Temporal constraints pose a challenge for conditional planning, because it is necessary for a conditional planner to determine whether a candidate plan will satisfy the specified temporal constraints. This can be difficult, because temporal assignments that satisfy the constraints associated with one conditional branch may fail to satisfy the constraints along a different branch. In this paper we address this challenge by developing the Conditional Temporal Problem (CTP) formalism, an extension of standard temporal constraint-satisfaction processing models used in non-conditional temporal planning. Specifically, we augment temporal CSP frameworks by (1) adding observation nodes, and (2) attaching labels to all nodes to indicate the situation(s) in which each will be executed. Our extended framework allows for the construction of conditional plans that are guaranteed to satisfy complex temporal constraints. Importantly, this can be achieved even while allowing for decisions about the precise timing of actions to be postponed until execution time, thereby adding flexibility and making it possible to dynamically adapt the plan in response to the observations made during execution. We also show that, even for plans without explicit quantitative temporal constraints, our approach fixes a problem in the earlier approaches to conditional planning, which resulted in their being incomplete.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44793/1/10601_2004_Article_5141764.pd