Deliberate Normative Agents

Boella, Guido; Lesmo, Leonardo

doi:10.1007/978-1-4615-1555-5_5

Cited by 18 publications

(14 citation statements)

References 10 publications

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“…As such, using the proposed techniques, an agent will be able to solve the MDP, learn the model of the environment, and generate norms if the confidence level is reached. The implementation framework that is used to code these ideas is the one developed by Dr. Sutton in the RLAI lab 4 . This framework provides the basic tools to implement any desired RL algorithm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Leveraging Domain Knowledge to Learn Normative Behavior: A Bayesian Approach

Hosseini

Ulieru

2012

Lecture Notes in Computer Science

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Abstract. This paper addresses the problem of norm adaptation using Bayesian reinforcement learning. We are concerned with the effectiveness of adding prior domain knowledge when facing environments with different settings as well as with the speed of adapting to a new environment. Individuals develop their normative framework via interaction with their surrounding environment (including other individuals). An agent acquires the domain-dependent knowledge in a certain environment and later reuses them in different settings. This work is novel in that it represents normative behaviors as probabilities over belief sets. We propose a two-level learning framework to learn the values of normative actions and set them as prior knowledge, when agents are confident about them, to feed them back to their belief sets. Developing a prior belief set about a certain domain can improve an agent's learning process to adjust its norms to the new environment's dynamics. Our evaluation shows that a normative agent, having been trained in an initial environment, is able to adjust its beliefs about the dynamics and behavioral norms in a new environment. Therefore, it converges to the optimal policy more quickly, especially in the early stages of learning.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Leveraging Domain Knowledge to Learn Normative Behavior: A Bayesian Approach

Hosseini

Ulieru

2012

Lecture Notes in Computer Science

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“…However, the authors did not specify a solution to meet these requirements. The work of Boella & Lesmo in [8], was one of the first proposals on the MAS field that provided a solution to the autonomous decision on norm compliance. Specifically, the authors provided some strategies for making a decision about norm compliance.…”

Section: Related Workmentioning

confidence: 99%

Reasoning about norms under uncertainty in dynamic environments

Criado¹,

Argente²,

Noriega³

et al. 2014

International Journal of Approximate Reasoning

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The behaviour of norm-autonomous agents is determined by their goals and the norms that are explicitly represented inside their minds. Thus, they require mechanisms for acquiring and accepting norms, determining when norms are relevant to their case, and making decisions about norm compliance. Up until now the existing proposals on norm-autonomous agents assume that agents interact within a deterministic environment that is certainly perceived. In practice, agents interact by means of sensors and actuators under uncertainty with non-deterministic and dynamic environments. Therefore, the existing proposals are unsuitable or, even, useless to be applied when agents have a physical presence in some real-world environment. In response to this problem we have developed the n-BDI architecture. In this paper, we propose a multi -context graded BDI architecture (called n-BDI) that models norm-autonomous agents able to deal with uncertainty in dynamic environments. The n-BDI architecture has been experimentally evaluated and the results are shown in this paper.

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“…Must research has been undertaken on how to incorporate norms into agents and multi-agent systems in taking steps toward the computational implementation of societies, institutions and organisations. This research has ranged from fundamental work on the importance of norms in agent behaviour [5,21], to proposing internal representations of norms [3,4,13], analysing the different types of norms [7,19], considering their emergence in groups of agents [22], proposing logics for their formalisation [18,23], and describing how agents manage norm adoption and compliance [2,8,14]. In the field of agents, research has primarily been focused at the level of multi-agent systems where norms represent the means to achieve coordination among their members.…”

Section: Introductionmentioning

confidence: 99%

A Model of Normative Multi-agent Systems and Dynamic Relationships

López

Lück

2004

Regulated Agent-Based Social Systems

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Abstract. For agents, one of the advantages of being part of a society is the satisfaction of those goals whose success depends on the abilities of other agents. In turn, societies are controlled by norms and, consequently, agents must be able first to model the society in which they exist, and then to identify the different relationships, due to norms, in which they might be involved in order to act appropriately. Both of these could mean the difference between the success or failure of their goals. To this end, this paper focuses on the identification of the basic components of norm-based systems, and on representing and analysing the dynamic relationships between member agents which result from the processing of norms.

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Deliberate Normative Agents

Cited by 18 publications

References 10 publications

Leveraging Domain Knowledge to Learn Normative Behavior: A Bayesian Approach

Leveraging Domain Knowledge to Learn Normative Behavior: A Bayesian Approach

Reasoning about norms under uncertainty in dynamic environments

A Model of Normative Multi-agent Systems and Dynamic Relationships

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