Abstract
Socio-technical systems constitute a challenge for multiagent systems as they are complex scenarios in which human and artificial agents share information, interact and make decisions. For example, the design of an airport requires to interface information coming from automatic apparatuses as security cameras, conceptual information coming from agents, and normative information which agents’ behavior must comply with. Thus, in order to design systems that are capable of assisting human agents in organizing and managing socio-technical systems, we need fine grained tools to handle several types of information. The aim of this paper is to discuss a general framework to describe socio-technical systems as cases of complex multiagent systems. In particular, we use a foundational ontology to address the problems of interoperability and conceptual analysis, we discuss how to interface conceptual information with low level information obtained by computer vision or perception, and we discuss how to integrate information coming from heterogeneous agents.
Supported by the VisCoSo project grant, financed by the Autonomous Province of Trento through the “Team 2011” funding programme.
We would like to thank the anonymous reviewers and the participants of the COIN workshop for their useful comments and suggestions.
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Notes
- 1.
Ideally, this is not the case, as in multiagent systems agents can be heterogeneous under many respects, including the adoption of different languages and also of different ontologies. The strong requirement should be that their ontologies are well founded, so that their underlying assumptions are explicit enough as to enable communication and exchange of information via “connecting axioms”. In the current paper, for the sake of simplicity, we will assume that all agents in the system share the same ontology, dolce.
- 2.
Quality spaces are related to the famous treatment of concepts in [15].
- 3.
The cognitive module of \(\textsc {dolce}\) has been discussed in [13].
- 4.
We present the idea for physical object. An analogous treatment, although more complex, can be defined for events and activities.
- 5.
This treatment presupposes the existence of the object \(x\) that provides the focus of a given camera. Moreover, we are assuming that the individual qualities that trigger the recognition of an object as a visual representation are qualities of the object itself and not of the image (e.g. video sequence). This is motivated by the fact that the existence of the physical object is assumed to be the “same” focus of possibly divergent observers. This assumption is conceptually possible in our scenario because the cameras are trained for detecting a particular object in a specific location.
- 6.
For an axiomatic definition of the predicates that we introduce, we refer to [22].
- 7.
Note that we do not want the knowledge base to be closed under negative information, namely we have to endorse an open world assumption on each \(A_{i}\). This is because the fact that a camera does not detect a man carrying a gun does not mean that we can claim that he is not carrying a gun.
- 8.
We are aware that the consistency assumption may be a highly demanding condition in case we model cognitive agents. We assume it here just for the sake of simplicity, in order to directly apply the model of the next section.
- 9.
We are thankful to an anonymous reviewer for stressing this point. We are aware that this is a demanding assumption. For example, synchronizing surveillance cameras and human agents’ communications may require interfacing two different time segmentations of events. We abstract from this issue in order to present our analysis of systemic information and in order to provide an easy application of social choice theoretic techniques.
- 10.
The methodology we propose is motivated by our intention of providing an analysis of the quality of systemic information depending on a number of parameters. Although the aggregation process is centralized, more plausible, and possibly feasible, distributed mechanisms that provide the same collective information can be defined. We leave this point for future work.
- 11.
These results depend on the structure of the language that the agents use. It is enough to include some minimal logical connection to generate inconsistent outcomes, cf. [20]. Even if the propositions in the agents’ sets are atomic, we are evaluating consistency wrt the ontology, that contains complex propositions.
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Porello, D., Setti, F., Ferrario, R., Cristani, M. (2014). Multiagent Socio-Technical Systems: An Ontological Approach. In: Balke, T., Dignum, F., van Riemsdijk, M., Chopra, A. (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems IX. COIN 2013. Lecture Notes in Computer Science(), vol 8386. Springer, Cham. https://doi.org/10.1007/978-3-319-07314-9_3
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