anticipatory coordination in socio-technical knowledge-intensive environments: behavioural implicit...

Anticipatory Coordinationin Socio-technical Knowledge-intensive Environments:

Behavioural Implicit Communication in MoK

Stefano Mariani, Andrea Omicini

Alma Mater Studiorum—Universita di Bologna, Italy

AIxIA 2015Ferrara, Italy

24th September 2015

Mariani, Omicini (Universita di Bologna) Anticipatory Coordination: BIC in MoK AIxIA 2015 – 24/9/2015 1 / 31

Outline

1 Premises

2 Ingredients

3 Towards Anticipatory Coordination

4 Experiment

5 Conclusion


Premises

Outline

1 Premises

2 Ingredients


4 Experiment

5 Conclusion


Premises

Challenges of Socio-technical Systems

Socio-technical systems (STS) arise when cognitive and socialinteraction is mediated by information technology, rather than by thenatural world (alone) [Whi06]

STS are heavily interaction-centred, so they need coordination at theinfrastructure level [MC94]

Coordination issues in STS are:

unpredictability “humans-in-the-loop”, whereas software behaviour isprogrammable and predictable, human’s is not

scale physically-distributed, open systems, large-scale,ever-increasing number of people, devices, data


Premises

Challenges of Knowledge-intensive Environments

Knowledge-intensive Environments (KIE) are workplaces in whichsustainability of the organisation long-term goals is influenced by theevolution of knowledge [Bha01]

Usually, KIE are computationally supported by STS, so they needcoordination too

Coordination issues in KIE are:

size massive amount of raw data, aggregated information,reification of procedures and best-practices, and the like

pace data is produced and consumed at a fast pace, hugefrequency of interactions


Premises

The Idea

Coordination models and technologies draw inspiration fromdistributed collective intelligence phenomena in natural systems,looking for self-organising and adaptive coordination mechanisms[VPB12, MZ09, VC09, ZCF+11, MO13]

A novel perspective

Similarly, we focus the “social layer” of STS, looking for novelcoordination approaches inspired by the latest cognitive and social sciencestheories of action.


Premises

The Approach

1 We take as a reference the M olecules of K nowledge (MoK )coordination model for knowledge self-organisation in KIE [MO13]

2 Extend it toward the notion of anticipatory coordination — as a formof collective intelligence arising by emergence

3 According to the theory of Behavioural Implicit Communication (BIC)[CPT10]


Ingredients

Outline

1 Premises

2 Ingredients


4 Experiment

5 Conclusion


Ingredients

Behavioural Implicit Communication

Implicit interaction

Behavioural implicit communication (BIC) is a form of implicit interactionwith no specialised signal conveying the message, since the message is thepractical behaviour itself — and possibly, its traces [CPT10].

BIC presupposes observation: agents should be able to observeothers’ actions (and traces), as well as to mind-read the intentionsbehind them

BIC applies to human beings, to both cognitive and non-cognitiveagents, and to computational environments as well [WOO07]

Smart environments are pro-active, intelligent working environmentsable to autonomously adapt their behaviour according to users’interactions [CPT10] 1

1Which is, not by chance, the very notion of anticipatory coordination.Mariani, Omicini (Universita di Bologna) Anticipatory Coordination: BIC in MoK AIxIA 2015 – 24/9/2015 9 / 31

Ingredients

Smart (Computational) Environments

In [TCR+05], an abstract model for smart environments defines twotypes of environment:

c-env common environment, where agents can observe onlythe state of the environment (including actions’ traces),not the actions of their peers

s-env shared environment, enabling different forms ofobservability of actions, and awareness of thisobservability

Three requirements can thus be devised:1 observability of agents’ actions and traces enabled by default2 the environment should be able to understand actions and their traces,

possibly inferring intentions and goals motivating them3 agents should be able to understand the effects of their activity on

both the environment and other agents, so as to opportunisticallyobtain a reaction


Ingredients

M olecules of K nowledge I

MoKM olecules of K nowledge (MoK ) is a coordination model promotingself-organisation of knowledge [MO13].

Inspired to biochemical tuple spaces [VC09] and stigmergiccoordination [Par06]

Main goals:1 self-aggregation of information into more complex heaps, possibly

reifying useful knowledge previously hidden2 autonomous diffusion of information toward the interested agents, that

is, those needing it to achieve their goals


Ingredients

M olecules of K nowledge II

A MoK -coordinated system is thus:

a network of MoK compartments (tuple-space like informationrepositories)in which MoK seeds (sources of information) autonomously injectMoK atoms (information pieces)atoms undergo autonomous and decentralised reactions

aggregate into molecules (composite information chunks)diffuse to neighbourhoodsgets reinforced and perturbed by usersdecay as time flows

reactions are influenced by enzymes (reification of users’ epistemicactions)and scheduled according to Gillespie’s chemical dynamics simulationalgorithm [Gil77]


Towards Anticipatory Coordination

Outline

1 Premises

2 Ingredients


4 Experiment

5 Conclusion



MoK Compartments as c-env

The only sort of smart environment enabled in MoK is c-env,mapped upon a compartment, because [MO13]:

1 no sharing of compartments is assumed2 enzymes are visible only to MoK reactions3 enzymes cannot diffuse, thus neighbourhoods cannot perceive them

So, MoK does not support s-env since there is no observable actionreification in shared environments

Also, support to c-env is limited to compartments – notneighbourhoods – since enzymes cannot diffuse



MoK Compartments as s-env

Compartment can now be shared among users

Enzymes arediversified to resemble the epistemic nature of the action they reifymade observable to users sharing the compartment they live inno longer consumed by reinforcement reaction, but now subject todecaynow generating traces through a deposit reaction

Traces are introduced as the MoK abstraction resembling (side)effects of actions

different in kind, according to their “father” enzymeobservable solely by MoK reactionssubject to diffusion, decay and to a novel enzyme-dependantperturbation reaction

MoK compartments extension leads to neighbourhoods representingc-env, where action traces may diffuse, becoming part of theenvironment as they participate MoK reactions



Enzymes and Traces as BIC Enablers I

Modelenzyme(species, s, mol)c

enzyme(species, s, mol ′) + molcrreinf−−−→ enzyme(species, s, mol ′) + molc+s

trace(enzyme, p, mol)c

trace(enzyme, p, mol ′) + molcrpert−−→ .exec (p, trace, mol)

enzymerdep−−→ enzyme + trace(enzyme, p[species], mol)

species defines the epistemic nature of the action

s strength of reinforcement

p the perturbation the trace wants to perform

.exec starts execution of perturbation p 2



Enzymes and Traces as BIC Enablers II

Reinforcement influences relevance of information according to the(epistemic) nature and frequency of their actions, so as to bettersupport them in pursuing their goals

Enzymes situate actions, e.g., at a precise time as well as in a precisespace

Mind-reading and signification by assuming that users manipulating agiven corpus of information are interested in that information morethan other

Perturbation influences location, content, any domain-specific trait ofinformation, according to users’ inferred goals, with the goal of easingand optimising their workflows

Traces enable the environment to exploit users’ actions (possibly,inferred) side-effects for the profit of the coordination process —promoting the distributed collective intelligence leading toanticipatory coordination

2Notice, p is implicitly defined by species, as highlighted by notation p[species].Mariani, Omicini (Universita di Bologna) Anticipatory Coordination: BIC in MoK AIxIA 2015 – 24/9/2015 17 / 31


Tacit Messages Steering Anticipatory Coordination I

Tacit messages [CPT10] describe the kind of messages a practicalaction (and its traces) may implicitly send to its observers:

presence “Agent X is here”. Since an action (trace) is observable in

shared compartments (neighbourhoods), any agent therein

becomes aware of X existence and location — likewise for the

environment.

intention “X plans to do action b”. If the agents’ workflow determines

that action b follows action a, peers (as well as the environment)

observing X doing a may assume X next intention to be “do b”.

Accordingly, the environment may decide to undertake anticipatory

coordination actions easing/hindering action b — e.g. because

action b is computationally expensive.

ability “X is able to do ai=1,...,n”. . . . . .



Tacit Messages Steering Anticipatory Coordination II

. . . . . .

opportunity “ [e1, . . . , en] is the set of pre-conditions for doing a”

accomplishment “X achieved S”

goal “X has goal g”

result “Result r is available”

Enabling Collective Intelligence

Since agents can undertake the above described inferences, MoKcompartments actually act as BIC-based enablers of distributed collectiveintelligence phenomena — e.g., anticipatory coordination emerging due toagent interaction.



Tacit Messages Steering Anticipatory Coordination III

In many STS, a set of fairly-common actions may be devised, in spiteof the diversity in scope of the software platforms — e.g. Facebookvs. Mendeley:

quote/share re-publishing or mentioning someone else’s informationcan convey, e.g., tacit messages presence, ability,accomplishment. If X shares Y ’s information through action a,

every other agent observing a becomes aware of existence and

location of both X and Y (1). The fact that X is sharing

information I from source S lets X ’s peers infer X can manipulate

S (3). If X shared I with Z , Z may infer, e.g., that X expects Z

to somehow use it (5).

like/favourite marking as relevant a piece of information can convey,e.g., tacit messages presence, opportunity.



Tacit Messages Steering Anticipatory Coordination IV

follow subscribing for updates regarding some piece ofinformation or some user can convey tacit messagesintention, opportunity.

search performing a search query to retrieve information canconvey, e.g., tacit messages presence, intention,opportunity.

Perturbation Actions

Accordingly, each tacit message may cause a perturbation actioncontributing to anticipatory coordination — e.g. send discovery messages,establish privileged communication channels, undertake coordinationactions enabling/forbidding some interaction protocol, notify users aboutavailability of novel information, etc.


Experiment

Outline

1 Premises

2 Ingredients


4 Experiment

5 Conclusion


Experiment

Simulated Scenario

Citizen journalism scenario 3

users share a MoK -coordinated IT platform for retrieving andpublishing news storiesusers have personal devices (smartphones, tablets, pcs, workstations),running the MoK middleware, which they use to search the ITplatform for relevant informationsearch actions can spread up to a neighbourhood of compartments —e.g., to limit bandwidth consumption, boost security, optimiseinformation location, etc.search actions leave traces the MoK middleware exploits to attractsimilar information, actually enacting anticipatory coordination

3Technical details of the simulations – e.g. tools used and simulation parameters – inthe full paper.


Experiment

Self-organising Adaptive Anticipatory Coordination

Figure: Whereas data is initially randomly scattered across workspaces, as soon as usersinteract clusters appear by emergence thanks to BIC-driven self-organisation. Whenever newactions are performed by catalysts, the MoK infrastructure adaptively re-organises the spatialconfiguration of molecules so as to better tackle the new coordination needs.


Conclusion

Outline

1 Premises

2 Ingredients


4 Experiment

5 Conclusion


Conclusion

Conclusion & Outlook

Although our experiment focusses on one specific pattern ofanticipatory coordination, results are more than encouraging,deserving further investigation

Efforts are currently devoted to fully implement and run a MoKcoordinated system on a large-scale scenario—a prototypeimplementation of MoK exists, but large-scale deployment has notbeen achieved, yet

Special care will be payed to the semantic similarity measure

ontology-based semantic matching is rather unfeasible in pervasive,mobile scenariospurely syntactical matching has too low expressivenessviable tradeoffs may be usage of wildcards, e.g. as in Java regularexpressions4, or of synonymy relationships, e.g. as done in [PVM+10]using WordNet5

4http://docs.oracle.com/javase/tutorial/essential/regex/5http://wordnet.princeton.edu


http://docs.oracle.com/javase/tutorial/essential/regex/

http://wordnet.princeton.edu

References

References I

Ganesh D. Bhatt.Knowledge management in organizations: Examining the interaction between technologies,techniques, and people.Journal of Knowledge Management, 5(1):68–75, 2001.

Cristiano Castelfranchi, Giovanni Pezzullo, and Luca Tummolini.Behavioral implicit communication (BIC): Communicating with smart environments via ourpractical behavior and its traces.International Journal of Ambient Computing and Intelligence, 2(1):1–12, January–March2010.

Daniel T. Gillespie.Exact stochastic simulation of coupled chemical reactions.The Journal of Physical Chemistry, 81(25):2340–2361, December 1977.

Thomas W. Malone and Kevin Crowston.The interdisciplinary study of coordination.ACM Computing Surveys, 26(1):87–119, 1994.


References

References II

Stefano Mariani and Andrea Omicini.Molecules of Knowledge: Self-organisation in knowledge-intensive environments.In Giancarlo Fortino, Costin Badica, Michele Malgeri, and Rainer Unland, editors,Intelligent Distributed Computing VI, volume 446 of Studies in Computational Intelligence,pages 17–22. Springer, 2013.

Marco Mamei and Franco Zambonelli.Programming pervasive and mobile computing applications: The TOTA approach.ACM Transactions on Software Engineering and Methodology (TOSEM), 18(4), July 2009.

H. Van Dyke Parunak.A survey of environments and mechanisms for human-human stigmergy.In Danny Weyns, H. Van Dyke Parunak, and Fabien Michel, editors, Environments forMulti-Agent Systems II, volume 3830 of Lecture Notes in Computer Science, pages163–186. Springer, 2006.

Danilo Pianini, Sascia Virruso, Ronaldo Menezes, Andrea Omicini, and Mirko Viroli.Self organization in coordination systems using a WordNet-based ontology.In Indranil Gupta, Salima Hassas, and Rolia Jerome, editors, 4th IEEE InternationalConference on Self-Adaptive and Self-Organizing Systems (SASO 2010), pages 114–123,Budapest, Hungary, 27 September–1 October 2010. IEEE CS.


References

References III

Luca Tummolini, Cristiano Castelfranchi, Alessandro Ricci, Mirko Viroli, and AndreaOmicini.“Exhibitionists” and “voyeurs” do it better: A shared environment approach for flexiblecoordination with tacit messages.In Danny Weyns, H. Van Dyke Parunak, and Fabien Michel, editors, Environments forMulti-Agent Systems, volume 3374 of Lecture Notes in Artificial Intelligence, pages215–231. Springer, February 2005.

Mirko Viroli and Matteo Casadei.Biochemical tuple spaces for self-organising coordination.In John Field and Vasco T. Vasconcelos, editors, Coordination Languages and Models,volume 5521 of Lecture Notes in Computer Science, pages 143–162. Springer, Lisbon,Portugal, June 2009.

Mirko Viroli, Danilo Pianini, and Jacob Beal.Linda in space-time: An adaptive coordination model for mobile ad-hoc environments.In Marjan Sirjani, editor, Coordination Models and Languages, number 7274 in LectureNotes in Computer Science, pages 212–229. Springer, 2012.


References

References IV

Brian Whitworth.Socio-technical systems.Encyclopedia of human computer interaction, pages 533–541, 2006.

Danny Weyns, Andrea Omicini, and James J. Odell.Environment as a first-class abstraction in multi-agent systems.Autonomous Agents and Multi-Agent Systems, 14(1):5–30, February 2007.

Franco Zambonelli, Gabriella Castelli, Laura Ferrari, Marco Mamei, Alberto Rosi, GiovannaDi Marzo, Matteo Risoldi, Akla-Esso Tchao, Simon Dobson, Graeme Stevenson, Yuan Ye,Elena Nardini, Andrea Omicini, Sara Montagna, Mirko Viroli, Alois Ferscha, SaschaMaschek, and Bernhard Wally.Self-aware pervasive service ecosystems.Procedia Computer Science, 7:197–199, December 2011.


Anticipatory Coordinationin Socio-technical Knowledge-intensive Environments:

Behavioural Implicit Communication in MoK

Stefano Mariani, Andrea Omicini

Alma Mater Studiorum—Universita di Bologna, Italy

AIxIA 2015Ferrara, Italy

24th September 2015
