scholarly journals Understanding Hohfeld and Formalizing Legal Rights: The Hohfeldian Conceptions and Their Conditional Consequences

Studia Logica ◽  
2019 ◽  
Vol 108 (1) ◽  
pp. 129-158 ◽  
Author(s):  
Réka Markovich
Keyword(s):  
Legal Theory ◽  
Formal Analysis ◽  
Comprehensive Analysis ◽  
Legal Rights ◽  
Fundamental Theory ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Different Types ◽  
Multi Agent ◽  
Judicial Reasoning

Abstract Hohfeld’s analysis (Fundamental Legal Conceptions as Applied in Judicial Reasoning, 1913, 1917) on the different types of rights and duties is highly influential in analytical legal theory, and it is considered as a fundamental theory in AI&Law and normative multi-agent systems. Yet a century later, the formalization of this theory remains, in various ways, unresolved. In this paper I provide a formal analysis of how the working of a system containing Hohfeldian rights and duties can be delineated. This formalization starts from using the same tools as the classical ones by Kanger and Lindahl used, but instead of focusing on the algebraic features of rights and duties, it aims at providing a comprehensive analysis of what these rights and duties actually are and how they behave and at saying something substantial on Power too—maintaining all along the Hohfeldian intentions that these rights and duties are sui generis and inherently relational.

Intelligent Traceability System of Containerized Goods

2013 ◽  
Vol 309 ◽  
pp. 241-251 ◽  
Author(s):  
Mourad Abed ◽  
Imen Charfeddine ◽  
Mounir Benaissa ◽  
Marta Starostka-Patyk
Keyword(s):  
Supply Chain ◽  
Intelligent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Traceability System ◽  
Transport Chain ◽  
Multimodal Transport ◽  
Literature Reviews ◽  
Different Types ◽  
Multi Agent

In recent year, many countries across in the world have made traceability a compulsory procedure in the Supply Chain. The Supply Chain is distributed collaborative environments involves the acquisition and use of extensive informational and physical flows. The flows management seems a complex task for the actors of the multimodal transport chain which the transport is the major driver in a Supply Chain. The literature reviews throws light on the traceability in the Supply Chain Management (SCM) shows the lack of interoperability and flexibility in data management systems hinders the work of traceability. And it introduces the importance and complexity of multimodal transport operations. To ensure effective traceability all along this chain, we relied on the agent paradigm and the ontology which facilitate the integration of goods data in order to exploit and reuse. Indeed, to ensure communication and interoperability of these data we relied on Multi-Agent Systems, due to their characteristics of autonomy, sociability and responsiveness that are generally associated. The Multi-Agent Systems can build flexible systems whose behaviors are complex and complicated due to the combination of different types of agents. With a focus on the importance of the concept of the traceability, the objective of this work is to propose an intelligent system for the traceability of containerized goods in the context of multimodal transport: Intelligent Traceability System of Containerized Goods (i-TSCG).

scholarly journals COMPOSITIONAL VERIFICATION OF MULTI-AGENT SYSTEMS: A FORMAL ANALYSIS OF PRO-ACTIVENESS AND REACTIVENESS

2002 ◽  
Vol 11 (01n02) ◽  
pp. 51-91 ◽  
Author(s):  
CATHOLIJN M. JONKER ◽  
JAN TREUR
Keyword(s):  
Formal Analysis ◽  
Information Gathering ◽  
Multi Agent System ◽  
Compositional Verification ◽  
Multi Agent Systems ◽  
Verification Method ◽  
Agent Systems ◽  
Information Agents ◽  
Multi Agent ◽  
Compositional Method

A compositional method is presented for the verification of multi-agent systems. The advantages of the method are the well-structuredness of the proofs and the reusability of parts of these proofs in relation to reuse of components. The method is illustrated for an example multi-agent system, consisting of co-operative information gathering agents. This application of the verification method results in a formal analysis of pro-activeness and reactiveness of agents, and shows which combinations of pro-activeness and reactiveness in a specific type of information agents lead to a successful cooperation.

scholarly journals Mean Square Consensus of Nonlinear Multi-Agent Systems under Markovian Impulsive Attacks

2021 ◽  
Vol 11 (9) ◽  
pp. 3926
Author(s):  
Huan Luo ◽  
Yinhe Wang ◽  
Xuexi Zhang ◽  
Peitao Gao ◽  
Haoxiang Wen
Keyword(s):  
Sufficient Conditions ◽  
System Stability ◽  
Consensus Problem ◽  
Mean Square ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Special Cases ◽  
Different Types ◽  
Multi Agent ◽  
Mean Square Consensus

This paper focuses primarily on the mean square consensus problem of a class of nonlinear multi-agent systems suffering from stochastic impulsive deception attacks. The attacks here are modeled by completely stochastic destabilizing impulses, where their gains and instants satisfy all distributions and the Markovian process. Compared with existing methods, which assume that only gains are stochastic, it is difficult to deal with systems with different types of random variables. Thus, estimating the influence of these different types on the consensus problem is a key point of this paper. Based on the properties of stochastic processes, some sufficient conditions to solve the consensus problem are derived and some special cases are considered. Finally, a numerical example is given to illustrate the main results. Our results show that the consensus can be obtained if impulsive attacks do not occur too frequently, and it can promote system stability if the gains are below the defined threshold.

scholarly journals Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems

2005 ◽  
Vol 24 ◽  
pp. 407-463 ◽  
Author(s):  
P. S. Dutta ◽  
N. R. Jennings ◽  
L. Moreau
Keyword(s):  
Information Sharing ◽  
Formal Analysis ◽  
Empirical Evaluation ◽  
Task Completion ◽  
Multi Agent Systems ◽  
Long Distance ◽  
State Information ◽  
Agent Systems ◽  
Systems Research ◽  
Multi Agent

Effective coordination of agents' actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. We then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littman's Q-routing and Stone's TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.

scholarly journals Analysis of coordinated behavior structures with multi-agent deep reinforcement learning

2020 ◽  
Author(s):  
Yuki Miyashita ◽  
Toshiharu Sugawara
Keyword(s):  
Reinforcement Learning ◽  
Division Of Labor ◽  
Social Norm ◽  
The Other ◽  
Multi Agent Systems ◽  
Direct Communication ◽  
Scarce Resources ◽  
Agent Systems ◽  
Different Types ◽  
Multi Agent

Abstract Cooperation and coordination are major issues in studies on multi-agent systems because the entire performance of such systems is greatly affected by these activities. The issues are challenging however, because appropriate coordinated behaviors depend on not only environmental characteristics but also other agents’ strategies. On the other hand, advances in multi-agent deep reinforcement learning (MADRL) have recently attracted attention, because MADRL can considerably improve the entire performance of multi-agent systems in certain domains. The characteristics of learned coordination structures and agent’s resulting behaviors, however, have not been clarified sufficiently. Therefore, we focus here on MADRL in which agents have their own deep Q-networks (DQNs), and we analyze their coordinated behaviors and structures for the pickup and floor laying problem, which is an abstraction of our target application. In particular, we analyze the behaviors around scarce resources and long narrow passages in which conflicts such as collisions are likely to occur. We then indicated that different types of inputs to the networks exhibit similar performance but generate various coordination structures with associated behaviors, such as division of labor and a shared social norm, with no direct communication.

scholarly journals A UNIFIED SPECIFICATION FORMAT FOR INTERLEVEL RELATIONS BETWEEN AGENT MODELS IN MULTIPLE ABSTRACTION DIMENSIONS

2012 ◽  
Vol 04 (01) ◽  
pp. 1250026 ◽  
Author(s):  
JAN TREUR
Keyword(s):  
Three Dimensions ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Temporal Abstraction ◽  
Levels Of Abstraction ◽  
Agent Systems ◽  
Different Types ◽  
Multi Agent ◽  
Multiple Levels ◽  
Abstraction Levels

Multi-agent systems for a certain application area can be modeled at multiple levels of abstraction. Interlevel relations are a means to relate models from different abstraction levels. Three dimensions of abstraction often occurring are the process abstraction, temporal abstraction, and agent cluster abstraction dimension. In this paper a unifying formalization is presented that can be used as a framework to specify interlevel relations for any of such dimensions. The approach is illustrated by showing how a variety of different types of abstraction relations between multi-agent system models can be formally specified in a unified manner.

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