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Online Handbook of Argumentation for AI: Volume 2
This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI).
Multi-Agent Systems based on Contextual Defeasible Logic considering Focus
This work presents a multi-agent model based on CDL that not only allows agents to reason with their local knowledge bases and mapping rules, but also allows agents to reason about relevant knowledge (focus) -- which are not known by the agents a priori -- in the context of a specific query.
Dealing with Incompatibilities among Procedural Goals under Uncertainty
The second way for calculating the strength of the argument is based on the cost of the plans (regarding the necessary resources) and the preference of the goals associated with the plans.
An Imprecise Probability Approach for Abstract Argumentation based on Credal Sets
In this paper, we tackle the problem of calculating the degree of uncertainty of the extensions considering that the probability values of the arguments are imprecise.
An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents
In the context of goals selection, agents should be able to explain the reasoning path that leads them to select (or not) a certain goal.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Resolving Resource Incompatibilities in Intelligent Agents
An intelligent agent may in general pursue multiple procedural goals simultaneously, which may lead to arise some conflicts (incompatibilities) among them.
Argumentation-based Agents that Explain their Decisions
Explainable Artificial Intelligence (XAI) systems, including intelligent agents, must be able to explain their internal decisions, behaviours and reasoning that produce their choices to the humans (or other systems) with which they interact.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
An Argumentation-based Approach for Identifying and Dealing with Incompatibilities among Procedural Goals
An intelligent agent may in general generate multiple pursuable goals, which may be incompatible among them.
