http://dl.gi.de/handle/20.500.12116/11102 2026-07-21T14:04:40Z 2026-07-21T14:04:40Z Klügl, Franziska http://dl.gi.de/handle/20.500.12116/11419 2018-03-20T10:33:09Z 2014-01-01T00:00:00Z

Beyond Distributed Artificial Intelligence Klügl, Franziska

2014-01-01T00:00:00Z Bulling, Nils http://dl.gi.de/handle/20.500.12116/11420 2018-03-20T10:33:09Z 2014-01-01T00:00:00Z

Special Issue on Multi-Agent Decision Making Bulling, Nils

2014-01-01T00:00:00Z Broersen, Jan http://dl.gi.de/handle/20.500.12116/11421 2018-03-20T10:33:09Z 2014-01-01T00:00:00Z

Responsible Intelligent Systems Broersen, Jan The 2013 ERC-consolidator project “Responsible Intelligent Systems” proposes to develop a formal framework for automating responsibility, liability and risk checking for intelligent systems. The goal is to answer three central questions, corresponding to three sub-projects of the proposal: (1) What are suitable formal logical representation formalisms for knowledge of agentive responsibility in action, interaction and joint action? (2) How can we formally reason about the evaluation of grades of responsibility and risks relative to normative systems? (3) How can we perform computational checks of responsibilities in complex intelligent systems interacting with human agents? To answer the first two questions, we will design logical specification languages for collective responsibilities and for probability-based graded responsibilities, relative to normative systems. To answer the third question, we will design suitable translations to related logical formalisms, for which optimised model checkers and theorem provers exist. All three answers will contribute to the central goal of the project as a whole: designing the blueprints for a formal responsibility checking system. To reach that goal the project will combine insights from three disciplines: philosophy, legal theory and computer science.

2014-01-01T00:00:00Z Hunter, A. Parsons, S. Wooldridge, M. http://dl.gi.de/handle/20.500.12116/11423 2018-03-20T10:33:09Z 2014-01-01T00:00:00Z

Measuring Inconsistency in Multi-Agent Systems Hunter, A.; Parsons, S.; Wooldridge, M. We introduce and investigate formal quantitative measures of inconsistency between the beliefs of agents in multi-agent systems. We start by recalling a well-known model of belief in multi-agent systems, and then, using this model, present two classes of inconsistency metrics. First, we consider metrics that attempt to characterise the overall degree of inconsistency of a multi-agent system in a single numeric value, where inconsistency is considered to be individuals within the system having contradictory beliefs. While this metric is useful as a high-level indicator of the degree of inconsistency between the beliefs of members of a multi-agent system, it is of limited value for understanding the structure of inconsistency in a system: it gives no indication of the sources of inconsistency. We therefore introduce metrics that quantify for a given individual the extent to which that individual is in conflict with other members of the society. These metrics are based on power indices, which were developed within the cooperative game theory community in order to understand the power that individuals wield in cooperative settings.

2014-01-01T00:00:00Z