SPIN is an efficient verification system for models of distributed software systems. It has been used to detect design errors in applications ranging from high-level descriptions of distributed algorithms to detailed code for controlling telephone exchanges. The paper gives an overview of the design and structure of the verifier, reviews its theoretical foundation, and gives an overview of significant practical applications.

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The model checker SPIN

Explanation in artificial intelligence: Insights from the social sciences

http://emamispnu.persiangig.com/Projects/Cloud%20Computing/science_2.pdf

Review: A survey on security issues in service delivery models of cloud computing

Our goal in this paper is to introduce and motivate a methodology, called Tropos,1 for building agent oriented software systems. Tropos is based on two key ideas. First, the notion of agent and all related mentalistic notions (for instance goals and plans) are used in all phases of software development, from early analysis down to the actual implementation. Second, Tropos covers also the very early phases of requirements analysis, thus allowing for a deeper understanding of the environment where the software must operate, and of the kind of interactions that should occur between software and human agents. The methodology is illustrated with the help of a case study. The Tropos language for conceptual modeling is formalized in a metamodel described with a set of UML class diagrams.

/pdf/tropos-an-agent-oriented-software-development-methodology-4cfpp4zfvj.pdf

Tropos: An Agent-Oriented Software Development Methodology

Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they provide a good substrate for creating large-scale data sharing, content distribution, and application-level multicast applications. These P2P overlay networks attempt to provide a long list of features, such as: selection of nearby peers, redundant storage, efficient search/location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, and anonymity. P2P networks potentially offer an efficient routing architecture that is self-organizing, massively scalable, and robust in the wide-area, combining fault tolerance, load balancing, and explicit notion of locality. In this article we present a survey and comparison of various Structured and Unstructured P2P overlay networks. We categorize the various schemes into these two groups in the design spectrum, and discuss the application-level network performance of each group.

/pdf/a-survey-and-comparison-of-peer-to-peer-overlay-network-cpcv2ij1hk.pdf

A survey and comparison of peer-to-peer overlay network schemes

Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed, and is not part of the planning domain. Consequently, the focus is on learning the transitions between states. Current approaches address neither the problem of learning new states of the planning domain, nor the problem of representing and updating the mapping between the real world perceptions and the states. In this paper, we drop such assumptions. We provide a formal framework in which (i) the agent can learn dynamically new states of the planning domain; (ii) the mapping between abstract states and the perception from the real world, represented by continuous variables, is part of the planning domain; (iii) such mapping is learned and updated along the “life” of the agent. We define and develop an algorithm that interleaves planning, acting, and learning. We provide a first experimental evaluation that shows how this novel framework can effectively learn coherent abstract planning models.

Learning Abstract Planning Domains and Mappings to Real World Perceptions.

This chapter illustrates the usefulness of planning techniques—namely, HTNs and Markov Decision Processes (MDPs), for the design of a high-level navigation controller for a mobile robot. The approach is by no means limited to navigation tasks. It can be pursued for a wide variety of robotics tasks, such as object manipulation and cleaning. However, to keep the presentation technically grounded with enough details about the sensory-motor functions controlled, it is necessary to illustrate a specific task. Several sensory-motor functions are presented and discussed in the chapter. An approach that exemplifies the use of planning techniques for synthesizing alternative plans and policies for a navigation task is described. Before that, the chapter introduces the reader to the important and mature area of path and motion planning.

Planning in Robotics

Much planning research has been motivated by the difficulty of producing complete and correct plans. This chapter examines how the decidability and complexity of classical planning varies depending on a variety of conditions. The chapter discusses the decidability and undecidability results, the complexity results, and the significance and limitations of the results. The chapter contains discussion and historical remarks. The results in this chapter are based largely on work by Bylander, Erol et al. , and Backstrom and Nebel. In the theorems and proofs, the chapter concentrates primarily on classical planning—that is, on planning problems expressed using the classical representation scheme. However, in most cases this is sufficient to establish results for set-theoretic and state-variable planning as well because of the equivalence among them. Classical planning and state-variable planning are essentially equivalent; and set-theoretic planning, ground classical planning, and ground state-variable planning are all essentially equivalent. The chapter also considers a variety of syntactic extensions and restrictions on classical planning, such as allowing the terms to contain function symbols, not allowing the operators to have negative preconditions, not allowing them to have negative effects, and so forth. These extensions and restrictions are also applicable to set-theoretic planning, with one exception: it makes no sense to allow function symbols in set-theoretic planning because the latter does not have terms.

Complexity of Classical Planning

Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed or learned offline, and it is not part of the planning domain. Consequently, the focus is on learning the transitions between states. In this paper, we drop such assumptions. We provide a formal framework in which (i) the agent can learn dynamically new states of the planning domain; (ii) the mapping between abstract states and the perception from the real world, represented by continuous variables, is part of the planning domain; (iii) such mapping is learned and updated along the "life" of the agent. We define an algorithm that interleaves planning, acting, and learning, and allows the agent to update the planning domain depending on how much it trusts the model w.r.t. the new experiences learned by executing actions. We define a measure of coherence between the planning domain and the real world as perceived by the agent. We test our approach showing that the agent learns increasingly coherent models, and that the system can scale to deal with models with an order of $10^6$ states.

Paolo Traverso

Papers

Learning Abstract Planning Domains and Mappings to Real World Perceptions.

Planning in Robotics

Complexity of Classical Planning

Learning abstract planning domains and mappings to real world perceptions

An IPC Track on Deliberative Acting: Moving the competition ahead towards more relevant scientific challenges