Using norms to control open multi-agent systems

Criado, Natalia

doi:10.3233/aic-130560

Cited by 9 publications

(6 citation statements)

References 157 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The modelling of permissions as exceptions to obligations and prohibitions has been used to justify violations under specific circumstances, (e.g. Oren et al (2010); Criado (2012)). It is also used as a means to handle the uncertainty and incompleteness of the knowledge of the environment the agents operate in (Alrawagfeh and Meneguzzi, 2014).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Practical reasoning with norms for autonomous software agents

Shams

Vos

Padget

et al. 2017

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Autonomous software agents operating in dynamic environments need to constantly reason about actions in pursuit of their goals, while taking into consideration norms which might be imposed on those actions. Normative practical reasoning supports agents making decisions about what is best for them to (not) do in a given situation. What makes practical reasoning challenging is the interplay between goals that agents are pursuing and the norms that the agents are trying to uphold. We offer a formalisation to allow agents to plan for multiple goals and norms in the presence of durative actions that can be executed concurrently. We compare plans based on decision-theoretic notions (i.e. utility) such that the utility gain of goals and utility loss of norm violations are the basis for this comparison. The set of optimal plans consists of plans that maximise the overall utility, each of which can be chosen by the agent to execute. We provide an implementation of our proposal in Answer Set Programming, thus allowing us to state the original problem in terms of a logic program that can be queried for solutions with specific properties.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In this section we specify what we refer to as a norm in this work. In order to provide a context for the norm specification we explain how our norm specification corresponds to the five elements identified by Criado (2012) that distinguish norm specification languages.…”

Section: Normsmentioning

confidence: 99%

Practical reasoning with norms for autonomous software agents

Shams

Vos

Padget

et al. 2017

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…Unlike ANN, in which the interaction of neurons is modeled by a variety of mathematical equations, MAS agents are autonomous, therefore, usually one or more agents are selected that model the network of connections between agents. Functioning each according to its own program, altogether the agents create the context of their collective intellectual interaction, which is usually called Swarm Intelligence (SI) [27,28]. Naturally, in such a system the coordination control problem (orchestration) arises.…”

Section: Artificial Intelligence Techniquesmentioning

confidence: 99%

Model of Self-organizing Knowledge Representation and Organizational Knowledge Transformation

Moroz¹

2020

AJAI

View full text Add to dashboard Cite

The purpose of the paper is development of a conceptual model for the representation of knowledge as an active intellectual substance and, on this basis, study of metaphysics of knowledge transformation process being produced both individually and collectively in the practice of organizations. The first principle of knowledge engineering, as Edward Albert Feigenbaum noted, says that the power in solving problems that an intellectual subject (person or machine) manifests in the process of activity depends primarily on its knowledge base, and only secondly on the methods of inference used. Strength is hidden in knowledge. The process of producing knowledge is permanent and does not depend on whether an individual is going to use this knowledge or not. Knowledge constantly produces new knowledge regardless of the owner's desire. Besides that, knowledge can't arise from nothing, but always -from some knowledge obtained earlier. As well as the intelligence, knowledge is an emergent instance arising from the collective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta). Idiosyncrasy of this interaction is expressed precisely in the creation of new knowledge. Due to postulating the knowledge self-organizing, the hierarchical knowledge structures in memory and the process of thinking as a kind of syntax for the procedure of new knowledge generation are described. This is an effort towards understanding the memory mechanisms, the process of thinking, the sources of heuristic knowledge just through the inner nature of knowledge. Also, based on the knowledge self-organization principle, an archetype of the appropriate knowledge-based system architecture is presented too. As an implementation of the concept, the perceptual act model is described, and on its base, a possible scenario for the behavior of a robot meeting an obstacle in its path is considered. As the mutual transformation of tacit and explicit knowledge makes new knowledge, the impact of the self-organization of knowledge on the transformation process as well as conditions of self-organization of both individual knowledge and organizational knowledge are analyzed in detail. Finally, modification of the known model of knowledge dimensions by Nonaka and Takeuchi is proposed. Because of the native activity of knowledge, it is impossible to build a knowledge management system without considering the internal structure of knowledge and its emergent ability to self-organize. Ensuring the natural process of knowledge development at all ontological levels in an organization is an essential prerequisite for the evolution of values in this organization.

show abstract

“…To improve robustness of coalitions in OMASs, various measures have been presented, including reputation systems to indicate the reliability of AAs [4], rule-sets to identify and punish malicious agents [6], and fault-tolerant consensus mechanisms to achieve coalition agreements, even if not all AAs are available [7]. Nevertheless, the presented measures have shortcomings that can decrease their effectiveness.…”

Section: Introductionmentioning

confidence: 99%

“…It introduces the concepts of organizations and institutions to model agent societies. (e.g., [6]). Social systems approaches (e.g., social circles [15]) draw on concepts from law and economics to improve social structures (e.g., [16]).…”

Section: Introductionmentioning

confidence: 99%

Tackling Challenges of Robustness Measures for Agent Collaboration in Open Multi-Agent Systems

Jin¹,

Kannengießer²,

Sturm³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Open multi-agent systems (OMASs) allow autonomous agents (AAs) to collaborate in coalitions to accomplish complex tasks (e.g., swarm robots exploring new terrain). In OMASs, AAs can arbitrarily join and leave the network. Thus, AAs must often collaborate with unknown AAs that may corrupt coalitions, leading to less robust systems. However, measures to improve robustness of OMASs are subject to challenges, decreasing their effectiveness. To understand how to improve coalition robustness in OMASs and address challenges of existing robustness measures, we carried out a literature review and revealed three types of robustness measures (i.e., collaboration coordination, normative control, and reliability prediction). Moreover, we found 21 challenges for the identified robustness measures and 24 corresponding solutions. By carrying out this literature review, we forge new connections between existing measures and identify challenges and measures that apply to multiple existing measures. Hereby, our work supports more robust collaborations between AAs in open systems.

show abstract

Using norms to control open multi-agent systems

Cited by 9 publications

References 157 publications

Practical reasoning with norms for autonomous software agents

Practical reasoning with norms for autonomous software agents

Model of Self-organizing Knowledge Representation and Organizational Knowledge Transformation

Tackling Challenges of Robustness Measures for Agent Collaboration in Open Multi-Agent Systems

Contact Info

Product

Resources

About