Improving probability selection based weights for satisfiability problems

Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively

Lower Bounds for Possibly Divergent Probabilistic Programs

We present a new proof rule for verifying lower bounds on quantities of probabilistic programs. Our proof rule is not confined to almost-surely terminating programs -- as is the case for existing rules -- and can be used to establish non-trivial lower bounds on, e.g., termination probabilities and expected values, for possibly divergent probabilistic loops, e.g., the well-known three-dimensional random walk on a lattice

Program Model Checking: A Practitioner's Guide

Program model checking is a verification technology that uses state-space exploration to evaluate large numbers of potential program executions. Program model checking provides improved coverage over testing by systematically evaluating all possible test inputs and all possible interleavings of threads in a multithreaded system. Model-checking algorithms use several classes of optimizations to reduce the time and memory requirements for analysis, as well as heuristics for meaningful analysis of partial areas of the state space Our goal in this guidebook is to assemble, distill, and demonstrate emerging best practices for applying program model checking. We offer it as a starting point and introduction for those who want to apply model checking to software verification and validation. The guidebook will not discuss any specific tool in great detail, but we provide references for specific tools

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

Shifting the Perspectival Landscape: Methods for Encoding, Identifying, and Selecting Perspectives

This dissertation explores the semantics and pragmatics of perspectival expressions. Perspective, or point-of-view, encompasses an individual’s thoughts, perceptions, and location. Many expressions in natural language have components of their meanings that shift depending on whose perspective they are evaluated against. In this dissertation, I explore two sets of questions relating to perspective sensitivity. The first set of questions relate to how perspective is encoded in the semantics of perspectival expressions. The second set of questions relate to how conversation participants treat perspectival expressions: the speaker’s selection of a perspective and the listener’s identification of the speaker’s perspective. In Part I, I explore the landscape of perspectival expressions by exploring different semantic mechanisms for encoding the perspective holder. In Chapter 2, I introduce key properties of perspectival expressions through a discussion of one canonical perspectival expression: the motion verb come. In Chapter 3, I discuss the various ways of encoding the perspective holder in the semantics of perspectival expressions. I contrast the predictions of these approaches and lay out a set of diagnostics to guide the analysis of perspectival expressions. I present two case studies using this set of diagnostics. In Chapter 3, I probe the semantics of the well-studied perspectival expression come in American English, and argue in favor of a perspective-anaphoric analysis. In Chapter 4, I focus on an expression that has not previously been recognized as perspectival, the temporal adverbial tomorrow. Through a series of experimental studies, I make the case that tomorrow is perspective-sensitive for some American English speakers, and narrow the hypothesis space for a perspectival account of tomorrow. I sketch a perspective-anaphoric semantics for tomorrow, while leaving open the possibility of a logophoric analysis. I conclude Part I with a discussion of how perspectival expressions fit into the broader landscape of context sensitivity. In Part II, I turn to a fresh set of questions about perspective: how do conversation participants select and identify perspectives? In Chapter 6, I discuss previous models of perspective production and comprehension, and factors that affect these processes, such as a bias towards the perspective of the speaker. I argue that although the selection and identification of perspective holders may be guided by simple heuristics some of the time, certain cases require a more involved reasoning system. In Chapters 7 and 8, I develop models of perspectival reasoning in comprehension and production rooted in a leading framework for pragmatic reasoning: the Rational Speech Acts framework. In Chapter 7, I propose and implement a computational model of perspective identification. I posit that listeners reason jointly about the speaker’s intended message and their adopted perspective using a mental model of the speaker’s production process. I present two comprehension studies that support a key assumption of the proposed Perspectival Rational Speech Acts model: that listeners reason simultaneously over multiple perspectives to better understand the speaker’s intended meaning. In Chapter 8, I propose a model of perspective selection that mirrors the Perspectival Rational Speech Acts comprehension model. I posit that speakers reason about the listener’s comprehension process in order to pick a perspective and an utterance that will maximize their chance of being understood. However, the results of the production study do not match the model’s predictions. I conclude with a discussion of the challenges that the attested asymmetry between speaker and listeners poses for the Rational Speech Acts framework. The main contributions of this dissertation are as follows: (1) a comparison of four approaches to encoding the semantics of perspective, leading to a diagnostic toolkit for perspectival expressions; (2) an experimental case study that employs the diagnostics to identify a novel perspectival expression; (3) an implemented computational model of perspective identification, supported by experimental evidence; and (4) an implemented computational model of perspective selection, which reveals further challenges in perspective production