A probabilistic argumentation framework for reinforcement learning agents

@article{Riveret2019APA,
  title={A probabilistic argumentation framework for reinforcement learning agents},
  author={R{\'e}gis Riveret and Yang Gao and Guido Governatori and Antonino Rotolo and Jeremy V. Pitt and Giovanni Sartor},
  journal={Autonomous Agents and Multi-Agent Systems},
  year={2019},
  volume={33},
  pages={216-274}
}
A bounded-reasoning agent may face two dimensions of uncertainty: firstly, the uncertainty arising from partial information and conflicting reasons, and secondly, the uncertainty arising from the stochastic nature of its actions and the environment. This paper attempts to address both dimensions within a single unified framework, by bringing together probabilistic argumentation and reinforcement learning. We show how a probabilistic rule-based argumentation framework can capture Markov decision… Expand
A probabilistic deontic argumentation framework
TLDR
This paper provides a possible answer to the question of what does it mean that something is probably obligatory and how does it relate to the probability that it is permitted or prohibited by merging deontic argumentation and probabilistic argumentation into a Probabilistic deontIC argumentation framework. Expand
A Deontic Argumentation Framework Towards Doctrine Reification
TLDR
It is shown then that bivalent statement labellings can fall short to address normative completeness, and for this reason, it is proposed to use trivalent labelling semantics. Expand
Towards Understanding and Arguing with Classifiers: Recent Progress
TLDR
A novel deep but tractable model for conditional probability distributions that can harness the expressive power of universal function approximators such as neural networks while still maintaining a wide range of tractable inference routines is reviewed. Expand
Nova: Value-based Negotiation of Norms
Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts,Expand
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
TLDR
This work introduces MARLeME: a MARL model extraction library, designed to improve explainability of MARL systems by approximating them with symbolic models. Expand
Data science applications for predictive maintenance and materials science in context to Industry 4.0
Abstract With the revolutionising of the industry to the next generations, machines have become more complicated. If they are not put to regular maintenance then there is more breakdown andExpand

References

SHOWING 1-10 OF 65 REFERENCES
Probabilistic rule-based argumentation for norm-governed learning agents
TLDR
This paper proposes an approach to investigate norm-governed learning agents which combines a logic-based formalism with an equation-based counterpart, which enables the reasoning of such agents and their interactions using argumentation, and to capture systemic features using equations. Expand
A labelling framework for probabilistic argumentation
TLDR
This work investigates a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account and provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature. Expand
Probabilistic abstract argumentation: an investigation with Boltzmann machines
TLDR
The construction of neuro-argumentative systems based on probabilistic argumentation is associated with a model of abstract argumentation and the graphical model of Boltzmann machines in order to couple the computational advantages of learning and massive parallel computation. Expand
Argumentation-based Normative Practical Reasoning
TLDR
A formal model for normative practical reasoning is introduced that allows an agent to plan for multiple and potentially conflicting goals and norms at the same time and justify the best plan via an argumentation-based persuasion dialogue for grounded semantics. Expand
Probabilistic Reasoning with Abstract Argumentation Frameworks
TLDR
A general framework to measure the amount of conflict of inconsistent assessments and provide a method for inconsistency-tolerant reasoning is presented. Expand
Argumentation Accelerated Reinforcement Learning for Cooperative Multi-Agent Systems
TLDR
This work defines AARL via argumentation and proves that it can coordinate independent cooperative agents that have a shared goal but need to perform different actions, and shows that it significantly improves upon standard RL. Expand
Neuro-Symbolic Agents: Boltzmann Machines and Probabilistic Abstract Argumentation with Sub-Arguments
TLDR
An abstract argumentation framework accounting for sub-arguments, but where the content of (sub-)arguments is left unspecified is considered, to make the ideas as widely applicable as possible. Expand
Practical reasoning as presumptive argumentation using action based alternating transition systems
TLDR
The contribution of the paper is to provide firm foundations for an approach to practical reasoning based on presumptive argument in terms of a well-known model for representing the effects of actions of a group of agents. Expand
An abstract framework for argumentation with structured arguments
  • H. Prakken
  • Mathematics, Computer Science
  • Argument Comput.
  • 2010
An abstract framework for structured arguments is presented, which instantiates Dung's (‘On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming, andExpand
A quantitative approach to belief revision in structured probabilistic argumentation
TLDR
This paper proposes the QAFO (Quantitative Annotation Function-based Operators) class of operators, a subclass of AFO, and goes on to study the complexity of several problems related to their specification and application in revising knowledge bases. Expand
...
1
2
3
4
5
...