scholarly journals Possession as Linear Knowledge

10.29007/ntkm ◽  
2018 ◽  
Author(s):  
Frank Pfenning
Keyword(s):  
Distributed System ◽  
Epistemic Logic ◽  
Linear Logic ◽  
Expressive Power ◽  
Cut Elimination ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Ongoing Effort ◽  
Multi Agent ◽  
Localized Knowledge

Epistemic logic analyzes reasoning governing localized knowledge, and is thus fundamental to multi- agent systems. Linear logic treats hypotheses as consumable resources, allowing us to model evolution of state. Combining principles from these two separate traditions into a single coherent logic allows us to represent localized consumable resources and their flow in a distributed system. The slogan “possession is linear knowledge” summarizes the underlying idea. We walk through the design of a linear epistemic logic and discuss its basic metatheoretic properties such as cut elimination. We illustrate its expressive power with several examples drawn from an ongoing effort to design and implement a linear epistemic logic programming language for multi-agent distributed systems.

scholarly journals Model Checking Temporal Epistemic Logic under Bounded Recall

2020 ◽  
Vol 34 (05) ◽  
pp. 7071-7078
Author(s):  
Francesco Belardinelli ◽  
Alessio Lomuscio ◽  
Emily Yu
Keyword(s):  
Model Checking ◽  
Incomplete Information ◽  
Epistemic Logic ◽  
Upper Bounds ◽  
Experimental Results ◽  
Model Checker ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Bounded Recall

We study the problem of verifying multi-agent systems under the assumption of bounded recall. We introduce the logic CTLKBR, a bounded-recall variant of the temporal-epistemic logic CTLK. We define and study the model checking problem against CTLK specifications under incomplete information and bounded recall and present complexity upper bounds. We present an extension of the BDD-based model checker MCMAS implementing model checking under bounded recall semantics and discuss the experimental results obtained.

Constrained Epistemic Extension on Agent Knowledge Acquisition

2013 ◽  
Vol 651 ◽  
pp. 943-948
Author(s):  
Zhi Ling Hong ◽  
Mei Hong Wu
Keyword(s):  
Epistemic Logic ◽  
Information Acquisition ◽  
Default Logic ◽  
Dynamic Epistemic Logic ◽  
Default Reasoning ◽  
Multi Agent Systems ◽  
Negotiation Strategy ◽  
Agent Systems ◽  
Multi Agent ◽  
Default Rules

In multi-agent systems, a number of autonomous pieces of software (the agents) interact in order to execute complex tasks. This paper proposes a logic framework portrays agent’s communication protocols in the multi-agent systems and a dynamic negotiation model based on epistemic default logic was introduced in this framework. In this paper, we use the constrained default rules to investigate the extension of dynamic epistemic logic, and constrained epistemic extension construct an efficient negotiation strategy via constrained epistemic default reasoning, which guarantees the important natures of extension existence and semi-monotonicity. We also specify characteristic of the dynamic updating when agent learn new knowledge in the logical framework. The method for the information sharing signify the usefulness of logical tools carried out in the dynamic process of information acquisition, and the distributed intelligent information processing show the effectiveness of reasoning default logic in the dynamic epistemic logic theory.

scholarly journals ABSTRACT DATA TYPES FOR KNOWLEDGE REPRESENTATION AND SPECIFICATION OF MULTI-AGENT SYSTEMS

2021 ◽  
Vol 2 (336) ◽  
pp. 48-55
Author(s):  
A. Satybaldiyeva ◽  
A. Ismailova ◽  
R. Moldasheva ◽  
A. Mukhanova ◽  
K. Kadirkulov
Keyword(s):  
Distributed System ◽  
Distributed Algorithm ◽  
Multiagent System ◽  
Social Software ◽  
Multi Agent Systems ◽  
Data Types ◽  
Agent Systems ◽  
Social Ability ◽  
Internal States ◽  
Multi Agent

Distributed system is a group of decentralized interacting executers. Distributed algorithm is the communication protocol for a distributed system that transforms the group into a team to solve some task. Multiagent system is a distributed system that consists of autonomous reactive agents, i.e. executers which internal states can be characterized in terms Believes (B), Desires (D), and Intentions (I). Multiagent algorithm is a distributed algorithm for a multiagent system. The article discusses the basic concepts of agents and multi-agent systems. Also, two problems of multi-agent algorithms for representing knowledge in the context of Social Software Engineering are considered. A number of new multi-agent algorithms are presented, and their correctness is proved. The main characteristics of agents are provided, such as autonomy, proactivity, social ability, and reactivity; also, agents can have such additional characteristics as persistence, reasonability, performance, mobility, personality, and rationality. A number of new multi-agent algorithms are presented, and their correctness is proved. Two statements have been proved for solving RAM and MRP problems. This time we address a social issue of agent anonymity and privacy in these algo-rithms.

scholarly journals Symbolic Synthesis of Fault-Tolerance Ratios in Parameterised Multi-Agent Systems

2018 ◽  
Author(s):  
Panagiotis Kouvaros ◽  
Alessio Lomuscio ◽  
Edoardo Pirovano
Keyword(s):  
Fault Tolerance ◽  
Epistemic Logic ◽  
Swarm Robotics ◽  
Experimental Results ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Multi Agent ◽  
Symbolic Synthesis

We study the problem of determining the robustness of a multi-agent system of unbounded size against specifications expressed in a temporal-epistemic logic. We introduce a procedure to synthesise automatically the maximal ratio of faulty agents that may be present at runtime for a specification to be satisfied in a multi-agent system. We show the procedure to be sound and amenable to symbolic implementation. We present an implementation and report the experimental results obtained by running this on a number of protocols from swarm robotics.

Formal Stepwise Development of Scalable and Reliable Multiagent Systems

2012 ◽  
pp. 58-74
Author(s):  
Denis Grotsev ◽  
Alexei Iliasov ◽  
Alexander Romanovsky
Keyword(s):  
Multiagent Systems ◽  
Distributed System ◽  
Large Scale ◽  
Formal Method ◽  
System Management ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Dynamically Reconfigurable ◽  
Stepwise Development ◽  
Multi Agent

This chapter considers the coordination aspect of large-scale dynamically-reconfigurable multi-agent systems in which agents cooperate to achieve a common goal. The agents reside on distributed nodes and collectively represent a distributed system capable of executing tasks that cannot be effectively executed by an individual node. The two key requirements to be met when designing such a system are scalability and reliability. Scalability ensures that a large number of agents can participate in computation without overwhelming the system management facilities and thus allows agents to join and leave the system without affecting its performance. Meeting the reliability requirement guarantees that the system has enough redundancy to transparently tolerate a number of node crashes and agent failures, and is therefore free from single points of failures. The Event B formal method is used to validate the design formally and to ensure system scalability and reliability.

scholarly journals Developing Tools for Networks of Processors

Triangle ◽  
2018 ◽  
pp. 25
Author(s):  
Alfonso Ortega de la Puente ◽  
Marina De la Cruz Echeandía ◽  
Emilio Del Rosal ◽  
Carmen Navarrete Navarrete ◽  
Antonio Jiménez Martínez ◽  
...  
Keyword(s):  
Dna Computing ◽  
Expressive Power ◽  
Natural Computing ◽  
Formal Description ◽  
Turing Machines ◽  
Multi Agent Systems ◽  
Lindenmayer Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Made In

A great deal of research eort is currently being made in the realm of so called natural computing. Natural computing mainly focuses on the denition, formal description, analysis, simulation and programming of new models of computation (usually with the same expressive power as Turing Machines) inspired by Nature, which makes them particularly suitable for the simulation of complex systems.Some of the best known natural computers are Lindenmayer systems (Lsystems, a kind of grammar with parallel derivation), cellular automata, DNA computing, genetic and evolutionary algorithms, multi agent systems, arti- cial neural networks, P-systems (computation inspired by membranes) and NEPs (or networks of evolutionary processors). This chapter is devoted to this last model.

scholarly journals Special Issue “Multi-Agent Systems”: Editorial

2020 ◽  
Vol 10 (15) ◽  
pp. 5329
Author(s):  
Stefano Mariani ◽  
Andrea Omicini
Keyword(s):  
Social Sciences ◽  
Supply Chain ◽  
Heterogeneous Systems ◽  
Expressive Power ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Environment Change ◽  
Agent Systems ◽  
Multi Agent ◽  
The Many

Multi-agent systems (MAS) are built around the central notions of agents, interaction, and environment. Agents are autonomous computational entities able to pro-actively pursue goals, and re-actively adapt to environment change. In doing so, they leverage on their social and situated capabilities: interacting with peers, and perceiving/acting on the environment. The relevance of MAS is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across many different application scenarios and business domains, ranging from logistics to social sciences, from robotics to supply chain, and more. The reason behind such a widespread and diverse adoption lies in MAS great expressive power in modeling and actually supporting operational execution of a variety of systems demanding decentralized computations, reasoning skills, and adaptiveness to change, which are a perfect fit for MAS central notions introduced above. This special issue gathers 11 contributions sampling the many diverse advancements that are currently ongoing in the MAS field.

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