When I started out as a newly hatched PhD student, back in the day, one of the first articles I read and understood (or at least thought that I understood) was Ray Reiter’s classic article on default logic (Reiter, 1980).This was some years after the famous ‘non-monotonic logic’ issue of Artificial Intelligence in which that article appeared, but default logic was still one of the leading approaches, a tribute to the simplicity and power of the theory. As a result of reading the article, I became fascinated by both default logic and, more generally, non-monotonic logics. However, despite my fascination, these approaches never seemed terribly useful for the kinds of problem that I was supposed to be studying—problems like those in medical decision making—and so I eventually lost interest. In fact non-monotonic logics seemed to me, and to many people at the time I think, not to be terribly useful for anything. They were interesting, and clearly relevant to the long-term goals of Artificial Intelligence as a discipline, but not of any immediate practical importance. This verdict, delivered at the end of the 1980s, continued, I think, to be true for the next few years while researchers working in non-monotonic logics studied problems that to outsiders seemed to be ever more obscure. However, by the end of the 1990s, it was becoming clear, even to folk as short-sighted as I, that non-monotonic logics were getting to the point at which they could be used to solve practical problems. Knowledge in action shows quite how far these techniques have come. The reason that non-monotonic logics were invented was, of course, in order to use logic to reason about the world. Our knowledge of the world is typically incomplete, and so, in order to reason about it, one has to make assumptions about things one does not know. This, in turn, requires mechanisms for both making assumptions and then retracting them if and when they turn out not to be true. Non-monotonic logics are intended to handle this kind of assumption making and retracting, providing a mechanism that has the clean semantics of logic, but which has a non-monotonic set of conclusions. Much of the early work on non-monotonic logics was concerned with theoretical reasoning, that is reasoning about the beliefs of an agent—what the agent believes to be true. Theoretical reasoning is the domain of all those famous examples like ‘Typically birds fly. Tweety is a bird, so does Tweety fly?’, and the fact that so much of non-monotonic reasoning seemed to focus on theoretical reasoning was why I lost interest in it. I became much more concerned with practical reasoning—that is reasoning about what an agent should do—and non-monotonic reasoning seemed to me to have nothing interesting to say about practical reasoning. Of course I was wrong. When one tries to formulate any kind of description of the world as the basis for planning, one immediately runs into applications of non-monotonic logics, for example in keeping track of the state of a changing world. It is this use of non-monotonic logic that is at the heart of Knowledge in action. Building on the McCarthy’s situation calculus, Knowledge in action constructs a theory of action that encompasses a very large part of what an agent requires to reason about the world. As Reiter says in the final chapter,

Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems

ion-Driven SAT-based Analysis of Security Protocols . . . . . . . . . . 257 Alessandro Armando, Luca Compagna A Case for Efficient Solution Enumeration . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 Sarfraz Khurshid, Darko Marinov, Ilya Shlyakhter, Daniel Jackson Cache Performance of SAT Solvers: a Case Study for Efficient Implementation of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Lintao Zhang, Sharad Malik Local Consistencies in SAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Christian Bessière, Emmanuel Hebrard, Toby Walsh Guiding SAT Diagnosis with Tree Decompositions . . . . . . . . . . . . . . . . . . . . 315 Per Bjesse, James Kukula, Robert Damiano, Ted Stanion, Yunshan Zhu On Computing k-CNF Formula Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 Ryan Williams Effective Preprocessing with Hyper-Resolution and Equality Reduction . . 341 Fahiem Bacchus, Jonathan Winter Read-Once Unit Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 Hans Kleine Büning, Xishun Zhao The Interaction Between Inference and Branching Heuristics . . . . . . . . . . . . 370 Lyndon Drake, Alan Frisch Hypergraph Reductions and Satisfiability Problems . . . . . . . . . . . . . . . . . . . 383 Daniele Pretolani Table of

Theory and Applications of Satisfiability Testing

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

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Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment.

Cloud computing has attracted significant attention due to the increasing demand for low-cost, high performance, and energy-efficient computing. Profit maximization for the cloud service provider (CSP) is a key objective in the large-scale, heterogeneous, and multi-user environment of a cloud system. This paper addresses the problem of minimizing the operation cost of a cloud system by maximizing its energy efficiency while ensuring that user deadlines as defined in Service Level Agreements are met. The workload in the cloud system can be modeled as independent batch requests or as task graphs with dependencies. This paper adopts the latter modeling approach, which provides more opportunities for energy and performance optimizations, thus enabling the CSP to meet user deadlines at lower operation costs. However, these optimizations require additional supporting efforts e.g., resource provisioning, virtual machine placement, and task scheduling, which are addressed in a holistic manner in the proposed framework. In the envisioned cloud environment, users can construct their own services and applications based on the available set of virtual machines, but are relieved from the burden of resource provisioning and task scheduling. The CSP will then exploit data parallelism in user workloads to create an energy and deadline-aware cloud platform.

An energy and deadline aware resource provisioning, scheduling and optimization framework for cloud systems

Cloud computing is a new and rapidly emerging computing paradigm where applications, data and IT services are provided over the Internet. The task-resource management is the key role in cloud computing systems. Task-resource scheduling problems are premier which relate to the efficiency of the whole cloud computing facilities. Task-resource scheduling problem is NP-complete. In this paper, we consider an approach to solve this problem optimally. This approach is based on constructing a logical model for the problem. Using this model, we can apply algorithms for the satisfiability problem (SAT) to solve the task-resource scheduling problem. Also, this model allows us to create a testbed for particle swarm optimization algorithms for scheduling workflows.

Task-resource scheduling problem

Composed of multiple modular robotic units, self-reconfigurable modular robots are metamorphic systems that can autonomously rearrange the modules and form different configurations depending on dynamic environments and tasks. The goal of self-reconfiguration is to determine how to change connectivity of modules to transform the robot from the current configuration to the goal configuration subject to restrictions of physical implementation. The existing reconfiguration algorithms use different methods, such as divide-and-conquer, graph matching, and the like, to reduce the reconfiguration cost. However, an optimal solution with a minimal number of reconfiguration steps has not been found yet. The optimal reconfiguration planning problem consists in finding the least number of reconfiguration steps transforming the robot from one configuration to another. This is an NP-complete problem. In this paper, we describe an approach to solve this problem. The approach is based on constructing logical models of the problem under study.

Programming for modular reconfigurable robots

Grid graphs are popular testbeds for planning with incomplete information. In particular, it is studied a fundamental planning problem, localization, to investigate whether gridworlds make good testbeds for planning with incomplete information. It is found empirically that greedy planning methods that interleave planning and plan execution can localize robots very quickly on random gridworlds or mazes. Thus, they may not provide adequately challenging testbeds. On the other hand, it is showed that finding localization plans that are within a log factor of optimal is NP-hard. Thus there are instances of gridworlds on which all greedy planning methods perform very poorly. These theoretical results help empirical researchers to select appropriate planning methods for planning with incomplete information as well as testbeds to demonstrate them. However, for practical application of difficult instances we need a method for their fast decision. In this paper we describe an approach to solve localization problem. This approach is based on constructing a logical model for the problem.

Localization on Discrete Grid Graphs

The set of parameterized k-covers problem

A self-aware system has the possibility of dealing with novel situations more effectively than a system without self-awareness. A selfaware system can attend to its own internal states, thus providing a means of generating introspection and self-modification capabilities. A robot needs a capability to attend to its internal states in order to be genuine self-aware. Internal states can be made up of emotion, belief, desire, intention and expectation or it can be processes such as sensation, perception, conception, simulation, action, planning and thought. It is crucially important to be aware of its own emotions, perceptions, beliefs and intentions during the recognition process. Currently, developments in the field of self-awareness of robots are mainly based on a mimicry of human internal states. It is difficult for systems developers to specify specific internal states for all possible conditions and situations. It is obvious that such systems have very limited opportunities

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Anna Gorbenko

Papers

Task-resource scheduling problem

Programming for modular reconfigurable robots

Localization on Discrete Grid Graphs

The set of parameterized k-covers problem

Robot Self-Awareness: Exploration of Internal States