the autonomy of automated systems: social systems and the multi-level autonomy

Social Systems and the Multi-level AutonomyThe Autonomy of Automated Systems

Andrea Omicini Giovanni Sartor

European University InstituteAlma Mater Studiorum—Universita di Bologna

Autonomous Weapons Systems – Law, Ethics, PolicyEuropean University Institute in Florence, Italy

24 April 2014

Omicini, Sartor (EUI & Univ. Bologna) Social Systems and the Multi-level Autonomy AWS-LEP – 24/4/2014 1 / 22

Outline

1 Agents & Multi-Agent Systems

2 Multi-level Autonomy

3 Conclusion


Agents & Multi-Agent Systems

Outline



3 Conclusion



Agents, Coordination, and Societies I

Complex systems as MAS

Nowadays, most of the complex computational systems of interestcan be thought, modelled, and built as multi-agent systems (MAS)[Zambonelli and Omicini, 2004]

In a MAS, many autonomous components (the agents) with theirown individual goal interact in order to achieve the overall designedsystem goal



Agents, Coordination, and Societies II

Agent societies & coordination

A group of agents coordinating in order to achieve some (local,non-individual) goal is called an agent society

Agent societies are built around coordination media[Gelernter and Carriero, 1992], encapsulating social (coordination)laws

Agent societies may have collective goals, possibly independent of theindividual agent goals [Ciancarini, 1996]

Examples: norms as coordination mechanisms enforced bycomputational institutions (aka electronic institutions)[Noriega and Sierra, 2002] working as the coordination abstractions



A Layered View of MAS I

Layering

From the software engineering viewpoint, agent societies represent alayering mechanism [Molesini et al., 2006]

There, agents, societies, individual and social goals are conceptualtools to be used at the most suitable level of abstraction

Each group of agents could be seen in principle as a single agent at ahigher level of abstraction

Viceversa, each agent could be modelled / built as an agent societyat deeper level of detail – with the global MAS level working as theuppermost layer

Accordingly, individual / social / global goals could in principle belayered in the same way



A Layered View of MAS II

Autonomous systems out of autonomous components

As a result, a MAS could be in principle conceived, designed, andbuilt as an autonomous system made of autonomous components

Even more, each agent society could be handled in the same way

So, autonomy could be conceived as a multi-level property ofcomputational systems designed as MAS, associated to each agent, toagent societies, and to the global MAS level as well


Multi-level Autonomy

Outline



3 Conclusion



Diverse Levels of Autonomy I

Independence

The key point here is the fact that conceptually there is no directdependence between the diverse levels of autonomy at the differentlevels of the MAS

In the case of coordinated systems, the coordination media couldembed

the reactive behaviour for an automatic coordinated behaviourthe implicit mechanisms for a teleonomic behaviour of the agent societythe operational plans for a teleologic social behaviour—for instance, byadopting ReSpecT tuple centres [Omicini and Denti, 2001] as thecoordination media

All this, independently of the level of autonomy of the individualagents composing the agent society



Diverse Levels of Autonomy II

Artefacts: making it more complex

Even more, a more articulated reference model for MAS, such as theagents & artefacts (A&A) meta-model [Omicini et al., 2008] couldmake the picture even more intricate

An A&A MAS is basically composed by agents and artefacts, where

artefacts are the tools that agents use to achieve their own goalsas such, they are typically automated, deterministic entities, which arenot required to be autonomous

So, for instance, by extending layering to include artefacts, any levelof a MAS could then feature any sort of automatic / autonomic /autonomous behaviour, essentially independently of any otherindividual / social / global property



Issues I

Classifying complex MAS

Classifying a complex MAS – either a software or a robotic one – asautomatic / teleonomic / teleologic is not necessarily a trivial task

It may require in general some articulated model

Arguably, a multi-level model of autonomy



Issues II

Global MAS level might not be enough

What if we assume that the global level of autonomy of a MAS is themost relevant system feature?

Issues such as responsibility and liability cannot a priori be reduced tothe simple observation of the main level of a MAS

Existing works on collective moral and responsibility typically refer tohuman groups, and just account for a two-level layering

For instance

an individual may participate to more than one MAS, possiblyexpressing different levels of autonomy in the diverse contextshowever, being a single component of more than one system, it couldin principle work as an element of inter-system interference, possiblyundetected, which could make issues like responsibility and liabilitymuch more complex



Issues III

Non-determinismThis is particularly the case of non-deterministic systems—for instance in the caseof stochastic behaviours in nature-inspired models

There, in fact, upper-level behaviour could appear by emergence without any linearconnection with the lower level components—as in the case of swarm systems

For instance, coordination media could used to encapsulate local interactionleading to self-organising behaviours [Ricci et al., 2007]

There, teleonomic components would self-organising around automaticabstractions such as tuple-based coordination media

The resulting behaviour would be essentially unpredictable

and be classified as either teleonomic – since it tends to autonomously preservesome essential system property – or even (possibly) teleologic—when coordinationartefacts would contain the local policies explicitly designed to produce the overallself-organising behaviour [Omicini et al., 2004]


Conclusion

Outline



3 Conclusion


Conclusion

Artificial Systems. . . I

. . . are not mere social systems

where humans and groups provide essentially two distinct levels ofabstraction

to be used for the attribution of properties

in artificial systems, many levels can be used, and possibly with thesame set of criteria

. . . are not natural systems

where the hierarchical view concerns fundamentally diverse layers

each one with its own entities and laws

in artificial systems, the many levels possibly available should bedescribed with a uniform set of criteria


Conclusion

Multi-level Autonomy. . .

. . . mandates for new concepts and tools

where artificial systems of any sorts could be understood and classified

according to their (possibly diverse) levels of autonomy

along with the many related concepts, such as (collective)responsibility and liability


Outline



3 Conclusion


Bibliography

Bibliography I

Ciancarini, P. (1996).Coordination models and languages as software integrators.ACM Computing Surveys, 28(2):300–302.

Gelernter, D. and Carriero, N. (1992).Coordination languages and their significance.Communications of the ACM, 35(2):97–107.

Molesini, A., Omicini, A., Ricci, A., and Denti, E. (2006).Zooming multi-agent systems.In Muller, J. P. and Zambonelli, F., editors, Agent-Oriented SoftwareEngineering VI, volume 3950 of Lecture Notes in Computer Science,pages 81–93. Springer Berlin Heidelberg.6th International Workshop (AOSE 2005), Utrecht, The Netherlands,25–26 July 2005. Revised and Invited Papers.


Bibliography

Bibliography II

Noriega, P. and Sierra, C. (2002).Electronic Institutions: Future trends and challenges.In Klusch, M., Ossowski, S., and Shehory, O., editors, CooperativeInformation Agents VI, volume 2446 of Lecture Notes in ArtificialIntelligence, pages 14–17. Springer Berlin Heidelberg.6th International Workshop (CIA 2002), Madrid, Spain,18–20 September 2002. Proceedings.

Omicini, A. and Denti, E. (2001).From tuple spaces to tuple centres.Science of Computer Programming, 41(3):277–294.


Bibliography

Bibliography III

Omicini, A., Ricci, A., and Viroli, M. (2008).Artifacts in the A&A meta-model for multi-agent systems.Autonomous Agents and Multi-Agent Systems, 17(3):432–456.Special Issue on Foundations, Advanced Topics and IndustrialPerspectives of Multi-Agent Systems.

Omicini, A., Ricci, A., Viroli, M., Castelfranchi, C., and Tummolini, L.(2004).Coordination artifacts: Environment-based coordination for intelligentagents.In Jennings, N. R., Sierra, C., Sonenberg, L., and Tambe, M., editors,3rd international Joint Conference on Autonomous Agents andMultiagent Systems (AAMAS 2004), volume 1, pages 286–293, NewYork, USA. ACM.


Bibliography

Bibliography IV

Ricci, A., Omicini, A., Viroli, M., Gardelli, L., and Oliva, E. (2007).Cognitive stigmergy: Towards a framework based on agents andartifacts.In Weyns, D., Parunak, H. V. D., and Michel, F., editors,Environments for MultiAgent Systems III, volume 4389 of LNCS,pages 124–140. Springer.3rd International Workshop (E4MAS 2006), Hakodate, Japan,8 May 2006. Selected Revised and Invited Papers.

Zambonelli, F. and Omicini, A. (2004).Challenges and research directions in agent-oriented softwareengineering.Autonomous Agents and Multi-Agent Systems, 9(3):253–283.Special Issue: Challenges for Agent-Based Computing.


Social Systems and the Multi-level AutonomyThe Autonomy of Automated Systems

Andrea Omicini Giovanni Sartor

European University InstituteAlma Mater Studiorum—Universita di Bologna

Autonomous Weapons Systems – Law, Ethics, PolicyEuropean University Institute in Florence, Italy

24 April 2014


the autonomy of automated systems: social systems and the multi-level autonomy

Technology

level of autonomy

multilevel autonomy

multilevel autonomy

bologna social systems

autonomous systems

agent society agent

global mas level

agent society omicini