Introduction: \nThe study work focuses on designing a smart contract language (named "Lazo") for the Bazo blockchain. The Bazo blockchain is a research blockchain to test different mechanisms and algorithms. In the current version, a Proof of Stake consensus algorithm and a virtual machine to execute Bazo intermediate language(opcodes) are integrated. However, writing smart contracts in Bazo opcodes is time \nconsuming and error-prone. The goal of this study work is to design a high-level language which is easier to read and write smart contracts. \n \nProcedure / Result: \nBefore designing Lazo, 24 existing smart contract languages are collected and roughly analyzed to identify the key characteristics of a language for the blockchain. Thereafter, three popular and well elaborated languages, namely Solidity, Vyper and Scilla, were analyzed in great detail. Their supported features, syntax and contract examples were also documented. With the acquired knowledge about smart contracts, Lazo language was designed in an agile manner. \n \nResult: \nAs a result, Lazo is designed to be a statically typed, imperative and non-turing complete programming language. All language features are documented with illustrative code snippets. The Lazo grammar is also written in ANTLR and \nverified with Java. Furthermore, contract examples from Solidity are translated to Lazo in order to prove that the real-world use cases can be programmed with Lazo \nas well. \n \nIn a follow-up thesis, a compiler could be developed to compile Lazo programs into Bazo virtual machine instructions.
Intelligent Cyber-physical systems can be modelled as multi-agent systems with planning capability to impart adaptivity for changing contexts. In such multi-agent systems, the protocol for plan execution must result in the proper completion and ordering of actions in spite of their distributed execution. However, in untrusted scenarios, there is a possibility of agents not respecting the protocol either due to faults or due to malicious reasons thereby resulting in plan failure. In order to prevent such situations, we propose to implement the execution of agents through smart contracts. This points to a generic architecture seamlessly integrating intelligent planning-based CPS and smart-contracts.
Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising advances in conversational AI to provide a chatbot-like mechanism for assisting agents in promptly resolving a customer's issue. In this paper, we develop a neural-based conversational solution that employs BiLSTM with attention mechanism and demonstrate how our system boosts the effectiveness of customer support agents. In addition, we discuss the design principles and the necessary considerations for our system. We then demonstrate how our system, named "Isa" (Intuit Smart Agent), can help customer service agents provide a high-quality customer experience by reducing customer wait time and by applying the knowledge accumulated from customer interactions in future applications.
Jul 1, 2018·2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Recently, despite the growing popularity of smart contracts, one serious concern is arising among both industry and academia, that is, whether they work autonomously without human intervention really as intended and, when we are not sure, how we can ensure that contracts meet particular requirements. To resolve this, we propose a new formal approach to smart contract development: Instead of defining contracts just as programs in conventional languages, they should be defined using formal logic so that we can verify whether they meet particular requirements and enforce them if necessary. The primary challenge is that expressive formal logic often turns out to be undecidable and consequently executable programs cannot be generated. As a solution, each contract definition is divided into two layers, namely specification layer in a decidable logic called Linear Dynamic Logic for verification and enforcement of requirements and rule layer for defining implementation details, while the consistency between the two layers is systematically guaranteed. Based on this, it also becomes possible to automatically generate executable contract programs from their formal specification, which leads to improving the trustworthiness of contracts. Evaluation on Hyperledger Fabric shows the feasibility and high effectiveness of our approach.
Blockchain technology has provided a platform for the decentralized execution of smart contracts. A smart contract is an agreement that is automatically executed when certain conditions have been met. The immutability, decentral nature, and consensus mechanisms that are characteristic to blockchain technology make the smart contract and its development cycle a new field of study in software engineering. A novel economic and defensive thinking is needed to develop workable, secure smart contracts. Motivated by the need for a novel approach to development, this thesis proposes a model-driven approach to smart contract development.\nModel-Driven Engineering (MDE) is an approach to information system development in which models and model technologies are applied to raise the level of abstraction at which developers create and evolve software, with the goal of both simplifying and formalizing the various activities and tasks that comprise the Software Development Life Cycle (SDLC). Model-Driven Architecture (MDA) is a framework for this approach. This thesis aims to apply this framework to create a method which describes the development phase from domain knowledge to smart contract foundation.\nThe creation of a method has two main aims, namely (i) to bridge the semantic gap between domain knowledge and smart contract by lowering the threshold for domain experts, and (ii) support developers in creating less vulnerable smart contracts that accurately represent the problem domain. This is done by constructing a model-driven method based on existing research that applies MDE to smart contract development. A literature study into this field yields the requirements and techniques for the method, which is consequently constructed based on these requirements and techniques.\nThe method is evaluated in twofold. First, the value is assessed through a case study, which shows that the developer benefits from a structured approach and the reduction of manual programming. Second, by an experiment which shows that people are better able to comprehend and communicate about models containing functional aspects of the smart contract if a computational independent model is included. By doing so it fulfills the aim of lowering the threshold for domain experts to participate in the smart contract development cycle.
This paper reports on the development and verification of a novel formal symbolic process virtual machine (FSPVM) for verifying the reliability and security of Ethereum smart contracts, denoted as FSPVM-E, completely in Coq proof assistant. It adopts execution-verification isomorphism (EVI), an extension of Curry-Howard isomorphism (CHI), as its fundamental theoretical framework. The current version of FSPVM-E is constructed on a general, extensible, and reusable formal memory (GERM) framework, an extensible and universal formal intermediate programming language Lolisa, which is a large subset of the Solidity programming language using generalized algebraic datatypes, and the corresponding formally verified interpreter of Lolisa, denoted as FEther. It supports the ERC20 standard and can automatically simultaneously symbolically execute the smart contract programs of Ethereum and verify their reliability and security properties using Hoare logic in Coq. In addition, this work, contributes to solving the problems of automation, inconsistency and reusability in higher-order logic theorem proving.
Vincenzo Scoca, Rafael Brundo Uriarte, Rocco De Nicola
A smart contract is the formalisation of an agreement, whose terms are automatically enforced by relying on a transaction protocol, while minimising the need of intermediaries. Such contracts not only specify the service and its quality but also the possible changes at runtime of the terms of agreement. Although smart contracts provide a great deal of flexibility, analysing their compatibility and reaching agreements with this level of dynamism is considerably more challenging, due to the freedom of clients and providers in formulating needs/offers. We introduce a formal language to specify interactions between offers and requests and present a methodology for the autonomous negotiation of smart contracts, which analyses the cost and the necessary changes for reaching an agreement. Moreover, we describe a set of experiments that provides insights on the relative cost of dynamism in negotiating smart contracts and compare the request/offer matching rates of our solution with related works.
Multi-agents systems communication is a technology, which provides a way for multiple interacting intelligent agents to communicate with each other and with environment. Multiple-agent systems are used to solve problems that are difficult for solving by individual agent. Multiple-agent communication technologies can be used for management and organization of computing fog and act as a global, distributed operating system. In present publication we suggest technology, which combines decentralized P2P BOINC general-purpose computing tasks distribution, multiple-agents communication protocol and smart-contract based rewards, powered by Ethereum blockchain. Such system can be used as distributed P2P computing power market, protected from any central authority. Such decentralized market can further be updated to system, which learns the most efficient way for software-hardware combinations usage and optimization. Once system learns to optimize software-hardware efficiency it can be updated to general-purpose distributed intelligence, which acts as combination of single-purpose AI.
The Internet of Things (IoT) is a current approach which aims to act on pervasive environments for connecting smart objects. These objects are capable to sense their own environment, interacting and cooperating with other devices. However, the IoT also requires to perform coordination actions that devices cannot carry out with the embedded electronic. In order to create smarter collaborative systems, the Internet of Agents (IoA) approach arises as an alternative for providing information and services to other objects, systems and people, and to initialize actions if necessary. In this paper, a semantic model of public agent contracts based on OWL ontologies enriched with Open Linked Data has been proposed as a fundamental way to describe standard collaborative units that can interact in a social context. The overarching ontology we have named IoA-OWL takes into account functional and non-functional aspects organized in six profiles such as agent, context, model, service, social, and object.
El Hassan Et-Tolba, Mohammed Ouassaid, Mohamed Maâroufi
In this paper we give an adaptation of the standard FIPA Contract net Protocol for a multi-agent based smart home simulation. Smart home appliances are controlled by software agents that can communicate their needs and un/satisfaction to the Home Energy Manager Agent. The problem we encountered with FIPA specification is that each interacting agent is either initiator or participant during his life cycle. We propose a solution that allows agents to change their basic behavior and to be dynamical to their whole environment change depending on their requirements. The proposed solution is suitable to manage flexible household appliances such as advancing, delaying, or suspending their operation. Hence it leads to building energy efficiency and enhancing agents' coordination and collaboration for smart grid demand side management.
Self-organization provides a suitable model for developing self-managed complex distributed systems, such as grid computing and sensor networks. Unlike current related studies, which propose only a single principle of self-organization, this mechanism synthesizes the three principles of self-organization: cloning/ spawning, resource exchange and relation adaptation. Based on this mechanism, an agent can autonomously generate new agents when it is overloaded, exchange resources with other agents if necessary, and modify relations with other agents to achieve a better agent network structure. In this way, agents can adapt to dynamic environments. The proposed mechanism is evaluated through a comparison with three other approaches, each of which represents state-of-the-art research in each of the three self-organization principles. Experimental results demonstrate that the proposed mechanism outperforms the three approaches in terms of the profit of individual agents and the entire agent network, the load-balancing among agents, and the time consumption to finish a simulation run. In addition, in a dynamic environment, it is nearly impossible to use a static, design time generated system structure for efficient problem solving. Instead, the system needs to be able to self-organize at runtime, which means that the components of the system are responsible for adapting themselves to suit the dynamic environment. Self-organization is usually defined as "the mechanism or the process enabling the system to change its organization without explicit external command during its execution time.
Kemas Muslim Lhaksmana, Yohei Murakami, Toru Ishida
The future of computing systems is leading to a growing complexity of heterogeneous computing systems integration situated in highly dynamic and uncertain environment. Self-organization has been proposed to be one of the solutions due to its autonomous capability to govern itself and to adapt upon changes. Developing self-organizing systems requires a suitable modeling method because of its unique characteristics, such as self-adaptive, decentralized, and emergent. Existing multiagent systems engineering methodologies lack modeling methods to design behavior adaptation for self-organizing systems. This paper proposes a role modeling method that allows designers to model how agents perform behavior adaptation at runtime by making transitions from one set of roles to another in decentralized manner. The behavior adaptation of each individual agent may eventually emerge as the adaptation of the organization. The proposed modeling method also helps designers to analyze issues that may occur because of playing multiple roles, such as role conflict, role overload, role transition oscillation, and other common issues in concurrency.
Electronic contracts are crucial for future e-Business models due to the increasing importance of Web services and the cloud as a reliable commodity enabling service-based value chains. Negotiation is the prerequisite for establishing a contract between two or more partners. These contracts are usually based on Service Level Agreements (SLAs). In this paper we present the framework of a smart Web service marketplace, which allows for automatic, autonomous, and adaptive negotiation and re-negotiation of Web services based on economic principles. Our approach enables market based service trading following a bazaar style and extends the classical supermarket approach typical for service negotiation today. We extend the WS-Agreement standard by feasible workflows to support auctioning for negotiation and re-negotiation. A specific highlight of our framework is the mapping of business strategies defined by economic goals of the respective organization into an ICT enabled framework. It facilitates autonomic agents acting as organizational representatives stipulating SLAs without human interaction. This allows for business transactions transparently to the environment but adhering to business objectives of the originating organization.
Torsten O. Paulussen, Armin Heinzl, Christian Becker
The health sector is a central domain in every economy. It is challenged by progressing costs and funding issues. Hospitals play a major role for the examination and treatment of patients. The sequence how patients are assigned to hospital units determines the quality of treatment, the resource utilization, as well as the patients’ overall treatment time. Thus, efficient scheduling of patients in hospitals is crucial. Current approaches disregard the decentral organization in hospitals and neglect the varying pathway of patients since they often focus on one single unit solely. We propose an agent-based coordination mechanism that overcomes these limitations. Patients and hospital resources are modeled as autonomous software agents which follow their own objectives. This reflects the decentralized structure in hospitals. Agents are coordinated by a distributed mechanism where software agents improve their situation through negotiations which moves towards an overall pareto-optimum. We show promising evaluations based on experiments.
The notion of Multi-Agent System (MAS) environment, as remarked by recent literature, has gained a key role, becoming a mediating entity, functioning as enabler but possibly also as a manager and constrainer of agent actions, perceptions, and interactions 1 while addressing the requirements of openness and scalability. According to such a perspective, the environment is not a merely passive source of agent perceptions and target of agent actions which is, actually, the dominant perspective in agency, but a first-class abstraction that can be suitably designed to encapsulate some fundamental functionalities and services, such as coordination and organization, besides agent mobility, communications, security, etc [2]. Then, the environment dimension appears to intersect with all the dimensions that should be addressed to define an agreement between autonomous agents, that is, all the different Agreement Technologies giving support to the building, development and management of agreements in decentralized and open systems between autonomous agents. Those dimensions are the ones related to the development of technologies dealing with: Semantics, Norms, Organizations, Argumentation & Negotiation, and Trust.
Ramachandra Kota, Nicholas Gibbins, Nicholas R. Jennings
Self-organizing multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralized approach for structural adaptation in explicitly modeled problem solving agent organizations. Based on self-organization principles, our method enables the autonomous agents to modify their structural relations to achieve a better allocation of tasks in a simulated task-solving environment. Specifically, the agents reason about when and how to adapt using only their history of interactions as guidance. We empirically show that, in a wide range of closed, open, static, and dynamic scenarios, the performance of organizations using our method is close (70–90%) to that of an idealized centralized allocation method and is considerably better (10–60%) than the current state-of-the-art decentralized approaches.
Multi-agent system (MAS) is a common way of exploiting the potential power of agent by combining many agents in one system. Each agent in a multivalent system has incomplete information and is in capable of solving entire problem on its own. Multi-agent system offers modularity. If a problem domain is particularly complex, large and contain uncertainty, then the one way to address, it to develop a number of functional specific and modular agent that are specialized at solving various problems individually. It also consists of heterogeneous agents implemented by different tool and techniques. MAS can be defining as loosely coupled network of problem solvers that interact to solve problems that are beyond the individual capabilities or knowledge of each problem solver. These problem solvers, often ailed agent are autonomous and can be heterogeneous in nature. MAS is followed by characteristics, Future application, What to be change, problem solving agent, tools and techniques used, various architecture, multi agent applications and finally future Direction and conclusion. Various Characteristics are limited viewpoint, effectively, decentralized; computation is asynchronous, use of genetic algorithms. It has some drawbacks which must be change to make MAS more effective. In the session of problem solving of MAS, the agent performance measure contains many factors to improve it like formulation of problems, task allocation, organizations. In planning of multivalent this paper cover self-interested multivalent interactions, modeling of other agents, managing communication, effective allocation of limited resources to multiple agents with managing resources. Using of tool, to make the agent more efficient in task that are often used. The architecture o MAS followed by three layers, explore, wander, avoid obstacles respectively. Further different and task decomposition can yield various architecture like BDI (Belief Desire Intension), RETSINA. Various applications of multi agent system exist today, to solve the real-life problems, new systems are being developed two distinct categories and also many others like process control, telecommunication, air traffic control, transportation systems, commercial management, electronic commerce, entertainment applications, medical applications. The future aspect of MAS to solve problems that are too large, to allow interconnection and interoperation of multiple existing legacy systems etc.