Mp-Set-222-Kn1p

SWARM CENTRIC SOLUTIONS FOR INTELLIGENT SENSOR NETWORKS A. Farina(1), L. Chisci(2), F. Fedi (3) (1) FIEEE, FIET, FREng, Fellow of EURASIP IEEE AESS Board of Governors CTIF Industry Advisory Chair Visiting Professor at UCL Dept Electronics Consultant, Land & Naval Defence Electronics Division, Leonardo Company, Roma, Italy (2) Dpt. of Information Engineering (DINFO) University of Florence (3) Land and Naval Defence Electronics Division, Leonardo Company, Roma, Italy

Key Note Speech ”Swarm Theoretical Concepts & Applications. An introduction”

SPECIALISTS’ MEETING - SET- 222 Venue: Centro Alti Studi Della Difesa, P.za Della Rovere, Roma Summary

 Specialists’ Meeting Topics  Definition of SWARM  Complexity of a swarm  Measuring complexity  Intelligence of a swarm  Books and papers defining the state of art  Enabling algorithms  Operational needs  Potential systems and applications  Way ahead

2 Specialists’ Meeting Topics:

 Swarm Operations: Does the swarm-centric sensor network and architecture extend the operational capabilities and range? For which use cases the swarm is more relevant than hierarchical structures? Which issues in the adoption of swarm of sensor in operation have been identified?  Swarm Applications: Which families of applications will benefit from the adoption of swarm concepts? Which innovative kind of applications will be enabled?  Swarm Systems Engineering: How to design, develop and validate a swarm- centric system? How to find the key performance indicators? How to handle dependability, security, interoperability, adaptivity, (self-) organization of the swarm of sensors? What kind of enhancements or of new protocols is needed?  Symbiotic Human-Swarm Interaction: How could a swarm of sensors improves situational awareness? How would the key design features of HCIs for swarms look like? Which degree of autonomy is desirable?  Power management and energy harvesting: Which methods/technologies/techniques are available to extend the operational lifetime of the swarm?  Modeling and Simulation (M&S): How does M&S improve the understanding of swarm behaviors?  Applied mathematics: What kind of maths is available to analyze swarms? How to estimate the computational load associated with swarm-centric algorithms?

 Signal processing: What kind of algorithms are suited best to be implemented on 3 (massive) sensor networks whose elements may have processing constrains? Summary

4 Definition of Swarm (https://en.wikipedia.org/wiki/Swarm_behaviour) Swarm behaviour, or swarming, is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction.

It is highly interdisciplinary topic.

Swarming is applied to insects, any other entity or animal that exhibits collective behaviour. The term flocking is used to refer to swarm behaviour in birds, herding to refer to swarm behaviour in quadrupeds, shoaling or schooling to refer to swarm behaviour in fish. ….

By extension, the term swarm is applied also to inanimate entities which exhibit parallel behaviours, as in a robot swarm, an earthquake swarm, or a swarm of stars.

Swarm behaviour is the collective motion of a large number of self-propelled entities.

It is an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. 5 METRONOMES Summary

6 Taxonomy of systems • Simple – They have a small number of components which have well-defined roles and are governed by well understood rules. • Complicated – They have a large number of components which have well- defined roles and are governed by well understood rules; 346 AM – Robustness is achieved through redundancy.

• Complex – They have a large number of similar components which may act according to rules that may change over time and that may not be well understood; – The connectivity of the components may be quite plastic and roles may be fluid; – Robustness is achieved by enabling the parts to adapt to the changing environment and adopt different roles; – Need to distinguish between complex system and complex dynamics (complex time behaviour may arise from simple systems). 7 Definition of “Complex System” • Complex systems contain many constituents; • constituents of a complex system are interdependent and interact non-linearly; • complex system possesses a structure spanning several spatial scales; • complex system is capable of emerging behavior (i.e. a self-organizing collective behaviour difficult to anticipate from a knowledge of agents’ behaviour).

Birds adapt to neighbouring behaviour and automatically form a flock

Internet map 8 (source “The Opte Project”, www.opte.org ) Complex Systems: Bibliography

9 Complex Systems: Bibliography

The illusion of complexity

10 Flying Robots: Beyond UAVs - Banquet speech, IEEE Radar Conference, Philadelphia, May 2016

―Flying robots can operate in three-dimensional, indoor and outdoor environments. However, many challenges arise as we scale down the size of the robot, which is necessary for operating in cluttered environments. I will describe recent work in VIJAY KUMAR developing small, autonomous Nemirovsky Family Dean of Penn Engineering robots, and the design and algorithmic challenges in the areas of (a) control and planning, (b) state estimation and mapping, and (c) coordinating large teams of robots. I will also discuss applications to search and rescue, first response and precision farming.‖

Publications and videos are The Phlone available at kumarrobotics.org. 11 The Change of Paradigm: the Ecological Approach. Mimicking Nature • The more we study the different phenomena, the more grows our consciousness of the relationship among them, i.e. the faced problems are systemic which means they are linked and depend on each others. • We need a new vision of the problems that is based upon a deep ecological awareness. This vision looks at the world as a network of phenomena which are deeply interconnected and depend on each others. • This paradigm implies the evolution from a hierarchical- centric view to a network-centric view of the complex systems. • The network-centric approach looks at the portions of a complex system, which can be systems themselves, as ecological community of entities which are related each other by a network of interdependencies. 12 Network-centric Paradigm: Organizational Features • Organizational Scheme – It is a key concept for a systemic analysis. The scheme describes a given system by defining its configuration of relationships among its components, i.e. it provides a description of the qualitative features of that system. – There are many studies which identify the network/graphs as the scheme for the life; similar considerations can lead to assume this scheme to be applicable to complex systems, the living entities being a specific case of complex systems. • The Feedback Loop – Each network extends in many directions as consequence circular, non linear relationships are highly probable. – The feedback loop is a circular configuration of causally interconnected elements, where an initial stimulus propagates itself along the ring connections so that each element acts onto the next one, till the last element acts onto the source of the stimulus. – The consequence of this configuration of relationships (scheme) is that

the first connection (input) is subjected to the effects of the last one 13 (output), which results into self-control capabilities of the whole system. Network-centric Paradigm: Key System Properties • Non-linear interaction: this can cause the rise to surprising and non-intuitive behavior on the basis of simple local co-evolution. This implies sensitivity to even small perturbations; • Decentralized control: the natural systems are not controlled centrally; there is no an “orchestra director”! • Self-organisation: natural systems can evolve over time to an attractor corresponding to a special state of the system, without the need for guidance from outside the system; • Non-equilibrium order: the natural systems, by their nature, proceed far from equilibrium.  Life is at the edge of chaos. Correlation of local effects is key; • Adaptation: the dynamic systems have to continually adapt and co- evolve in a changing environment; • Collective dynamics: the ability of elements to locally influence each other, and for these effects to ripple through the system, permits continual feedback among the evolving states of the system elements. 14 Summary

Summary

15 The Measures of Complexity (1/5) How many bits of information are needed to describe each pattern?

The complexity of a pattern (how many bits of information are needed to describe it) isn't always obvious. The upper left panel shows 128x 128 = 16,384 squares that are randomly colored black or white, which typically can't be described using less than 16,384bits. The smaller pieces of this pattern (top middle and right) consist of ewer random squares and therefore require fewer bits to describe. The lower left pattern, on the other hand, can be generated by a very short (100bit, say) program, because it's simply the binary digits of square 2 (0=black, 1=white square). Describing the bottom middle pattern requires an additional 14 bits just as the one above it, the pattern is so short that it doesn't help to specify that it's part of square 2.

In summary, we have seen that the whole can contain less information than the sum of its parts, and sometimes even less than one of its parts!

16 From Max Tegmark book. Measures of Complexity (2/5)

The right image is also simple, being merely a tiny part of the left one. But it is slightly more complex, requiring another 8 bytes to specify, with a 20-digit number, which of 10^20 different parts it is. So once again, we see that less is more, in the sense that the apparent information content rises when we restrict our attention to a small part of the whole thus losing the symmetry and the simplicity that was inherent in the totality of all parts taken together. For an even simpler example of this, consider that the algorithmic information content of a typical trillion-digit number is substantial, since the shortest program that prints it can't so much better than simply store all its trillion digits. Nonetheless, the list of all numbers 1,2,3,..... can be generated by quite a trivial computer program, so the complexity of the whole set is smaller than that of a typical member.

17 From Max Tegmark book Measures of Complexity (3/5) Current qualitative and quantitative definitions of complexity are ambiguous.

Quantitative measures of complexity include:

Kolmogorov complexity  the length of the shortest binary computer program that describes the object.

Cyclomatic complexity  the number of linearly independent control paths of the software program. (approx. 50% of our systems are software!)

Plecticity (*) refers to the ability of a connected set of actors to act synergistically via the connectivity between them.

(*) Murray Gell-Mann, ―The Quark and the Jaguar: Adventures in the Simple and the Complex‖, 1994.

Murray Gell-Mann, 1969 Nobel Prize in physics for his work on the theory of elementary particles (quark in particular). 18 Measures of Complexity (4/5)

Inet 3037 Rectangular lattice

Node number: 3037 Node number: 3037 Branch number: 4788 Branch number: 5964 The node degree distribution follows a power law!

(*) S. Jamin, J. Winick: Inet-3.0: Internet topology generator, Technical Report CSETR-456-02, Electrical Engineering & Computer Science

EECS Department, University of Michigan, 2002, http://topology.eecs.umich.edu/inet/inet-3.0.pdf. 19 Measures of Complexity (5/5)

• Betweenness centrality (BC) of a node v: average of the percentages of the minimal paths which link all the pairs of nodes, s and d, in the network graph, and which cross the node v. • Mean betweenness centrality of the graph: betweeness centrality averaged over all the nodes v in the graph.

• A possible definition for the plecticity of the graph is the ratio between the maximum value of the BC (that provides the most important hub of the graph) and the mean value of BC: maximal BC Thanks to Prof. F. Zirilli (Univ. of Plecticity  i.e. plecticity grows as Rome La Sapienza) - and mean BC larger hubs appear in colleagues - with whom the seminal work on domino effect and the network. phase transition of mechanical statistics was carried out.

20 Summary

Summary

21 From Shannon Information to Intelligence … to exploit in SWARM

100 y anniversary of C. Shannon birthday

22 BIRTH CERTIFICATE OF THE INFORMATION AGE: THE ANNUS MIRABILIS 1948

Channel Capacity: N(T) is the total number of different possible sequences of symbols that can be sent in the time interval T.

n Message Entropy: H(X )   p(xi )log2 p(xi ) i1  p(x, y)  Mutual Information: I(X;Y )   p(x, y)log  yY xX  p(x) p( y) 

C. E. Shannon, ―A mathematical theory of communication‖, Bell System Technical Journal, vol. 27, pp. 379–423 and 623–656, July and October 1948. 23 THE INFORMATION, JAMES GLEICK (*)

Publisher: FOURTH ESTATE LTD L’informazione – Una storia. Una teoria. Un diluvio. Published: 31 March 2011 James Gleick Feltrinelli 2012

(*) Author also of book on “Chaos”. 24 INFORMATION THEORETICAL TOOLS FOR GRAPHS: HOW TO MEASURE INTELLIGENCE

Emergent behaviors typically arise through formation of patterns not reducible to single agents behaviors.

Dynamic entropy.

Evolution of entropy of a dynamical system may highlight the appearance of patterns.

Example of dynamic entropy of diffusion (consensus) mechanism over undirected / directed graphs [1] .

- decrease of joint entropy evidences the creation of patterns,

- diffusion processes over directed graphs enable more structured patterns  clusters, global consensus systems  maybe it is not by chance that our brain neural

network is a directed graph! Entropy clustered consensus system

25 [1] S. Barbarossa, «Distributed Processing in Wireless Sensor Network’’, Plenary Talk at IEEE SAM, Darmstadt 2008. About 247.000.000 results

Intelligence describes the properties of mind in its ability to learn, analyze, understand, communicate, plan, reason, hypothesize, infer, construct abstract thought, and solve problems.

Intelligence as organized information that can be employed by an intelligent mind in exercising the above mentioned abilities.

Question: Can information theoretical tools evolve in a theory of intelligence?

Limits of Shannon’s theory.

Recent developments:

- Network information theory,

- Development of new metrics to analyze emergent behaviors,

- Analysis and design of a communication system for optimal transfer of intelligence must include the notion of significance.

In conclusion: A clear definition of Intelligence seems far to be found! It should encompass various sources of data: speech, images, social behavior, human gesture, text, audio signals, etc.

From: Biing Hwang Juang, «Quantification and Transmission of Information and Intelligence—History and Outlook‖, IEEE SPM, 26 pp. 90-101, July 2011. SWARM INTELLIGENCE (Wikipedia) https://en.wikipedia.org/wiki/Swarm_intelligence

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, either natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

G. Beni, author of 'From Swarm Intelligence to Swarm Robotics' in the book Swarm Robotics

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems. 27 Swarm (self-)regulation

• Stigmergy – Mass recruitment via a chemical trail. The recruiter and recruited are not physically in contact with each other. Communication is instead via modulation of the environment: the trail. The recruiter deposits a pheromone on the way back from a profitable food source and recruits simply follow that trail. • Labour Division – Division of labor is an important feature of colonial life in many species of social insects. One of the most striking aspects of division of labor is plasticity: the ratios of workers performing the different tasks that maintain the colony's viability and reproductive success can vary (i.e., workers switch tasks) in response to internal perturbations or external challenges.

28 SWARM INTELLIGENCE: LANGTON ANT

Very simple rules produce seemingly unpredictable results  potential unpredictable behaviour of (large) software programs Langton's ant is a two-dimensional Turing machine with a very simple set of rules but complicated C. Langton, one of the founders of artificial emergent behavior. life algorithms (1987), Santa Fe Institute.

The ant starts out on a grid containing black and white cells, and then follows the following set of rules.

1. If the ant is on a black square, it turns right and moves forward one unit.

2. If the ant is on a white square, it turns left and moves forward one unit.

3. When the ant leaves a square, it inverts the color.

When the ant is started on an empty grid, it eventually builds a "highway" that is a series of 104 steps that repeat indefinitely, each time displacing the ant two pixels vertically and horizontally. The plots above show the ant starting from a completely white grid after 386 (left figure) and 10647 (right figure) steps. The fact that the ant's path is unbounded is guaranteed by the Cohen-Kung theorem. It is believed that no matter what initial pattern the ant is started on, it will eventually build a highway (although it might in principle take an extremely long time to reach this point). This would appear to follow naturally from the fact that Langton's ant is reversible, although it remains formally unproved (Beermann and Van Foeken). 29 MATHEMATICAL TECHNIQUES TO COLLECTIVE INTELLIGENCE OF A SWARM The Collective Intelligence Factor c (for measurement)

Predictive validity and further potential connections to individual intelligence Example algorithms: 1.1 Particle swarm optimization (PSO) 1.2 Ant colony optimization 1.3 Artificial bee colony algorithm 1.4 Differential evolution 1.5 The bees algorithm 1.6 Artificial immune systems 1.7 Bat algorithm 1.8 Glowworm swarm optimization 1.9 Gravitational search algorithm 1.10 River formation dynamics 1.11 Self-propelled particles 1.12 Stochastic diffusion search 1.13 Multi-swarm optimization https://en.wikipedia.org/wiki/Swarm_intelligence https://en.wikipedia.org/wiki/Collective_intelligence

30 Summary

31 Bonabeau et al. Dorigo et al. Arquilla et al. 2001 2004 2000

Bonabeau et al., 1999

Brueckner Ed. 2005 32 REFERENCES

From IEEE SPM, May 2016, pp.8-11. 33 • F. Fedi, ―Benefits of Interoperable Open Architecture for Integration of Robotics & Autonomous Systems‖, Polaris Innovation Journal, Selex ES, n.23,2015 • F. Fedi, "Data-centric Multirobot Systems", Modelling and Simulation for Autonomous Systems Workshop (MESAS 2014), May 5-6 2014 • F. Fedi, A. Cignoni, ―Hybrid Worlds for Multi-Robot System Simulation‖, Polaris, Special Issue on Modeling & Simulation. • F. Fedi, ―The Real Time Swarm Intelligence Platform‖, Polaris, pp. 12-16, Selex Sistemi Integrati, October 2012. • F. Fedi, ―Swarm-centric Multirobot System: The Sistemi Software Integrati Solution‖, IEEE AESS European Conference on Satellite Telecommunications, October 5, 2012

Special Issue on: Polaris Autonomous & Lunch-time Unmanned Systems for Seminar Land, Underwater, Air and Space

34 Summary

35 Multiagent systems for collective tasks

36 Communication graph

37 Communication graph

A: Adjaciency matrix

38 Multiagent dynamics

39 Cohesiveness

40 Applications

Synchronisation of metronomes! 41 Lyapunov-based approach  A kind of system energy!

42 Lyapunov-based control under integrator dynamics

43 Rendezvous problem

44 General (non integrator) dynamics

45 Swarm-centric Formation Control Parallel formation

46 Parallel formation

47 Parallel formation

48 Circular formation

49 Circular formation

50 Swarm-centric Source Seeking

51 Source seeking

52 Source Seeking via Particle Swarm Optimization (PSO) Source seeking problem

53 PSO for source seeking

54 PSO for source seeking

55 Robot motion model

56 Simulations results

Simulation with Noise Concentration with Noise

57 Simulation results

PSO with Noise Concentration PSO with Noise

58 Self-Localization

59 Self-Localization

60 Kilobot: a simple robotic platform for swarm robotics

61 Kilobot

62 OverHead Controller (OHC)

63 Communication

64 Implementation

65 Testbed

66 Projection

67 Self-Localization

Positions

68 Self-Localization and motion

Pursuit

69 Source-seeking algorithm

Seeking

70 Source seeking via gradient estimation

71 Source seeking via gradient estimation

72 Source seeking via gradient estimation

73 Source seeking via gradient estimation

74 Swarm-centric surveillance

75 Random-set multiagent SLAM

Finite Random-Set Theory by R. Mahler for MTT ! 76 Multiagent PHD-SLAM algorithm

77 Multiagent PHD-SLAM algorithm

78 Fusion of map PHDs

79 Fusion of map PHDs

80 Multiagent PHD-SLAM: a numerical example

81 Simulation results

OSPA: Optimal Sub Pattern Assignement, by Schumaher et al. 82 Acknowledgments

83 References and inspirations

Summary S.p.A  Specialists’ Meeting Topics -  Definition of SWARM

 Complexity of a swarm Finmeccanica

 Measuring complexity -  Intelligence of a swarm  Books and papers defining the state of art  Enabling algorithms

 Operational needs Leonardo | 2016©  Potential systems and applications  Way ahead

85 The complexity of future defence scenarios

• The Chief Analyst of the UK Defence Science and Technology Laboratory (Dstl), Roger Forder, makes the following point in his discussion of the future of defence analysis: "One effect of the human element in conflict situations is to bring a degree of

complexity into the situation such that the emergent behaviour of the system as S.p.A a whole is extremely difficult to predict from the characteristics and relationships - of the system elements. [omissis] Usable theories of complexity, which would allow understanding of emergent

behaviour rather than merely its observation, would therefore have a great deal Finmeccanica to offer to some of the central problems facing defence analysis. Indeed they - might well be the single most desirable theoretical development that we should seek over the next few years.“ FORDER, “The Future of Defence Analysis.” Journal of Defence Science. 5, 2000, No. 2. pp. 215-226. • Due to its ―complex‖ nature a swarm system can provide solutions to cope with the Leonardo | 2016© ―complexity‖ of many current and future military scenarios. • The applications of swarm technology to military systems are in the infancy of realization, although clear benefits from the enhanced capabilities can be envisioned for missions such as: persistent search, long-term monitoring, sensor data collection, distributed networks, object retrieval, and offensive attack missions. 86 Swarming Operations*

(*) J. Arquilla, D. Ronfeldt, “Swarming and the Future of Conflict”, RAND Co., 2000.

Ubiquitous Stealthy Sustainable S.p.A sensing ubiquity pulsing -

The small size and The ability to repeatedly dispersed deployment

The capacity to provide strike the adversary Finmeccanica of units of maneuver - the surveillance and from all directions, then will help to convey an synoptic-level to dissever from the image simultaneously observations to create attack, redisperse, stealthy and ubiquitous. and maintain of and repeat the cycle as The squad will be “topsight.” battle conditions amorphous, at least to require. Leonardo | 2016© the eyes of the enemy.

Ubiquitous Sensing

S.p.A

Finmeccanica

Stealthy ubiquity: Threat Detection

S.p.A

Finmeccanica

Stealthy ubiquity: Alarm the neighbours

S.p.A

Finmeccanica

Sustainable Pulsing

S.p.A

Finmeccanica

Threat Managed

S.p.A

Finmeccanica

Ubiquitous Sensing: Stealthy Ubiquity

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Finmeccanica

93 Summary

 Specialists’ Meeting Topics S.p.A  Definition of SWARM -  Complexity of a swarm

 Measuring complexity Finmeccanica  Intelligence of a swarm -  Books and papers defining the state of art  Enabling algorithms  Operational needs

94 Human Swarm Squad Mobile C&C

INFO CMD

S.p.A

Finmeccanica

Fixed C&C 95 Robot Swarm

Mission Human- Adaptability Human- Robot Dependability Human-Robot Interaction Robot S.p.A

Interaction - Safety DynamicTask Assignment Interaction

RoboticOperating Data Distribution Service (DDS) DDS System

Linux Android PHYSICAL WORLD DEMO Finmeccanica -

Robotic& AutonomousSystem Node OperatorPersonal

COTS Components Device

Available Components

Planned Components © 2016 | Leonardo | 2016©  Multimission – Multiplatform (ROS-based)  Adaptive to dynamic environment / scenarios  Decentralised Dynamic Task Allocation HYBRID WORLD DEMO  Robot-Robot Cooperation/Coordination  Robot Failure Tolerant (Fault Robot Detection and Recovery at System Level)

Swarm-centric “Sense & Respond” Systems

S.p.A - Human Swarm Robot Swarm

Sensor Swarm

Finmeccanica - SquadraVeicolo OperatorePresidiato

Mobile Control Center

Mission Planning Center Site Control Center

97 Summary

 Specialists’ Meeting Topics S.p.A -  Definition of SWARM  Complexity of a swarm

 Measuring complexity Finmeccanica  Intelligence of a swarm -  Books and papers defining the state of art  Enabling algorithms  Operational needs  Potential systems and applications Leonardo | 2016©  Way ahead

98 SET-222: SWARM CENTRIC SOLUTIONS FOR INTELLIGENT SENSOR NETWORKS Background: Swarms of sensors are decentralized, self-organizing sensory systems and may be thought of as sensors networks where nodes may cooperate to carry out

tasks addressing common goals. This can be accomplished by dynamically, and S.p.A autonomously, changing their mutual relationships depending on the current - environment status. The quality factors which better characterize a swarm-centric system typically are: autonomy, adaptivity, self-organization, robustness,

scalability, modularity. Finmeccanica - The main relevance to NATO is that, from the human operator viewpoint, swarm-centric intelligent sensor networks improve human situational awareness acting as an ubiquitous sensory system which may adapt its performance to changes in both environment and operational status.

Objectives: Leonardo | 2016© The proposed Specialists’ Meeting aims at answering the following question: ―What specific features new sensing and actuating platforms should exhibit to best exploit the swarm-centric paradigm?‖ To provide a qualified answer to the question, experts from different fields and of different topics will address theoretical, operative and systemic issues concerning the design and usage of 99 swarm-centric intelligent sensor networks.

S.p.A

Finmeccanica

100

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101

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102 Thanks for your applause, another swarm phenomenon

Your questions are welcome

THANK YOU FOR YOUR ATTENTION The Information, Book Review by Sergio Verdú

104 IEEE SIGNAL PROCESSING MAGAZINE [99] JULY 2011

105 IEEE SIGNAL PROCESSING MAGAZINE [100-101] JULY 2011

106 SOME ORDER DISORDER CONSIDERATIONS IN LIVING SYSTEMS QUANTITATIVELY MEASURED IN TERMS OF INFORMATION AND ENTROPY

Example of the Shannon Information measure of a bacteria See reference «Order disorder ….»

Sintetizzare l’articolo e i suoi risultati in questa slide!

107 INTELLIGENCE

About 247.000.000 results

Intelligence describes the properties of mind in its ability to learn, analyze, understand, communicate, plan, reason, hypothesize, infer, construct abstract thought, and solve problems. Intelligence as organized information that can be employed by an intelligent mind in exercising the above mentioned abilities.

Question: Can information theoretical tools help to better understand cognitive systems ?

Limits of Shannon’s theory: In conclusion: A clear- singledefinition channel (one sourceof Intelligence / one destination) seemssource-channelfar toseparationbe found! theorem, It should- relevant metrics:encompass single source entropy,various channelsources capacity (bits/sec/Hz),of data: speech, images, social - operates at syntactic level, no involvement of semantic level . behavior, human gesture, text, audio signals, etc. Recent developments:

- Network information theory:

- multiple source/destinations  no source/channel separation theorem, - relevant metrics: multiple source entropy, transport capacity (bits/sec/Hz/m^2), - complex trade-off between competition for resources and cooperation for the common good.

- Development of new metrics to analyze emergent behaviors:

- predictive information = uncertainty about future – uncertainty about future, given the past, - efficiency of prediction = How much can be predicted / how difficult is to predict.

- Analysis and design of a communication system for optimal transfer of intelligence must include the notion of significance.

From: Biing Hwang Juang, «Quantification and Transmission of Information and Intelligence—History and Outlook‖, IEEE SPM, pp. 90-101, July 2011. 108 REFERENCES

• E. Bonabeau, M. Dorigo and G. Theraulaz ―Swarm Intelligence: From Natural to Artificial Systems‖, New York, NY: Oxford University Press, Santa Fe Institute Studies in the Sciences of Complexity, 1999; • M. Dorigo, T.Stutzle, ―Ant Colony Optimization‖, MIT Press, 2004 ; • E. Bonabeau, C. Meyer, Swarm intelligence: A whole new way to think about business, Harvard business review 79 (5), 2001. • J. Arquilla and D. Ronfeldt. ―Swarming and the Future of Conflict‖, Santa Monica, CA: RAND Corporation, 2000. • Sven A. Brueckner, ―Engineering Self-Organising Systems: Methodologies and Applications‖, Springer Science & Business Media, 2005

109 REFERENCES

• A. Farina, A. Graziano, F. Mariani, F. Zirilli, ―A cooperative sensor network: optimal deployment and functioning‖, special session, "Advanced situation assessment" of COGIS 09 (Paris), 16-18 November 2009. Oral presentation and on CD.

• A. Farina, A. Graziano, F. Mariani, F. Zirilli, ―A Cooperative Sensor Network: Optimal Deployment and Functioning‖, RAIRO OPERATIONS RESEARCH VOL. 44, no. 4, October-December 2010, SPECIAL ISSUE ON COGIS 2009, pp. 379-388.

• A.Farina, A. Graziano F. Mariani, F. Zirilli, ―A network centric solution of the deployment and assignment problems for a cooperative agent network‖, Polaris Innovation Journal, Selex Sistemi Integrati, no. 8, special issue on Large Systems, December 2011, pp. 35-40. • Vol. 08 - Nov. 2011 - M. Evangelista, A. Farina, A. Graziano, F. Mariani, F. Zirilli, "A Network Centric solution for a Cooperative Agent Network"

110 SWARM INTELLIGENCE

Swarm Intelligence is inspired by social animal and exploits system properties such as multitude, and autonomous behaviours to design systems whose capabilities enact from the (networked) organization of their elements.

111