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Lecture III: Collective Behavior of Multi -Agent Systems: Analysis Zhixin Liu Complex Systems Research Center, Academy of Mathematics and Systems Sciences,

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Presentation on theme: "Lecture III: Collective Behavior of Multi -Agent Systems: Analysis Zhixin Liu Complex Systems Research Center, Academy of Mathematics and Systems Sciences,"— Presentation transcript:

1 Lecture III: Collective Behavior of Multi -Agent Systems: Analysis Zhixin Liu Complex Systems Research Center, Academy of Mathematics and Systems Sciences, CAS

2 In the last lecture, we talked about Complex Networks Introduction Network topology Average path length Clustering coefficient D egree distribution Some basic models Regular graphs: complete graph, ring graph Random graphs: ER model Small-world networks: WS model, NW model Scale free networks: BA model Concluding remarks

3 Lecture III: Collective Behavior of Multi -Agent Systems: Analysis Zhixin Liu Complex Systems Research Center, Academy of Mathematics and Systems Sciences, CAS

4 Outline Introduction Model Theoretical analysis Concluding remarks

5 What Is The Agent? From Jing Hans PPT

6 What Is The Agent? Agent: system with two important capabilities: Autonomy: capable of autonomous action – of deciding for themselves what they need to do in order to satisfy their objectives Interactions: capable of interacting with other agents - the kind of social activity that we all engage in every day of our lives: cooperation, competition, negotiation, and the like. Examples: Individual, insect, bird, fish, people, robot, … From Jing Hans PPT

7 Multi-Agent System (MAS) MAS Many agents Local interactions between agents Collective behavior in the population level More is different.---Philp Anderson, 1972 e.g., Phase transition, coordination, synchronization, consensus, clustering, aggregation, …… Examples: Physical systems Biological systems Social and economic systems Engineering systems … …

8 Flocking of Birds Bee Colony Ant Colony Biological Systems Bacteria Colony

9 Engineering Systems

10 From Local Rules to Collective Behavior Phase transition, coordination, synchronization, consensus, clustering, aggregation, …… scale-free, small-world Crowd Panic pattern swarm intelligence A basic problem: How locally interacting agents lead to the collective behavior of the overall systems?

11 Outline Introduction Model Theoretical analysis Concluding remarks

12 Modeling of MAS Distributed/Autonomous Local interactions/rules Neighbors may be dynamic May have no physical connections

13 A Basic Model This lecture will mainly discuss

14 Each agent has the tendency to behave as other agents do in its neighborhood. Assumption makes decision according to local information ;

15 Vicsek Model (T. Vicsek et al., PRL, 1995) r A birds Neighborhood Alignment: steer towards the average heading of neighbors Motivation: to investigate properties in nonequilibrium systems A simplified Boid model for flocking behavior.

16 Notations Neighbors: x i (t) : position of agent i in the plane at time t v: moving speed of each agent r: neighborhood radius of each agent : heading of agent i, i= 1,…,n. t=1,2, …… r

17 Vicsek Model Neighbors: Position: Heading:

18 Vicsek Model Neighbors: Position: Heading:

19 Vicsek Model Neighbors: Position: Heading: is the weighted average matrix.

20 Vicsek Model

21 Some Phenomena Observed (Vicsek, et al. Physical Review Letters, 1995) a) ρ= 6, ε= 1 high density, large noise c ) b) ρ= 0.48, ε= 0.05 small density, small noise d) ρ= 12, ε= 0.05 higher density, small noise n = 300 v = 0.03 r = 1 Random initial conditions

22 Synchronization Def. 1: We say that a MAS reach synchronization if there exists θ, such that the following equations hold for all i, Question: Under what conditions, the whole system can reach synchronization?

23 Outline Introduction Model Theoretical analysis Concluding remarks

24 (0) x (0) x (1) x (2) G(0) (1) (2) G(1) …… G(2) (t-1) (t) x (t-1) x (t) G(t-1) …… Positions and headings are strongly coupled Neighbor graphs may change with time Interaction and Evolution

25 Degree: Volume: Average matrix: Degree matrix: Laplacian: Adjacency matrix: If i ~ j Otherwise Some Basic Concepts

26 Connectivity: There is a path between any two vertices of the graph. Connectivity of The Graph

27 Joint Connectivity: The union of {G 1,G 2,……,G m } is a connected graph. Joint Connectivity of Graphs G1G1 G2G2 G 1 G 2

28 Product of Stochastic Matrices Stochastic matrix A=[a ij ]: If j a ij =1; and a ij 0 SIA ( Stochastic, Indecomposable, Aperiodic ) matrix A If where Theorem 1: (J. Wolfowitz, 1963) Let A={A 1,A 2,…,A m }, if for each sequence A i1, A i2, …A ik of positive length, the matrix product A ik A i(k-1) … A i1 is SIA. Then there exists a vector c, such that

29 The Linearized Vicsek Model A. Jadbabaie, J. Lin, and S. Morse, IEEE Trans. Auto. Control, 2003.

30 Related result: J.N.Tsitsiklis, et al., IEEE TAC, 1984 Joint connectivity of the neighbor graphs on each time interval [th, (t+1)h] with h >0 Synchronization of the linearized Vicsek model Theorem 2 (Jadbabaie et al., 2003)

31 The Vicsek Model Theorem 3: If the initial headings belong to (- /2, /2), and the neighbor graphs are connected, then the system will synchronize. § Liu and Guo (2006CCC), Hendrickx and Blondel (2006). § The constraint on the initial heading can not be removed.

32 Example 1:

33 Connected all the time, but synchronization does not happen. Differences between with VM and LVM.

34 Example2 :

35 The neighbor graph does not converge May not likely to happen for LVM

36 How to guarantee connectivity? What kind of conditions on model parameters are needed ?

37 Random Framework Random initial states: 1) The initial positions of all agents are uniformly and independently distributed in the unit square; 2) The initial headings of all agents are uniformly and independently distributed in [- +ε, -ε] with ε (0, ).

38 Random Graph G(n,p): all graphs with vertex set V={1,…,n} in which the edges are chosen uniformly and independently with probability p. P.Erdős,and A. Rényi (1959) Not applicable to neighbor graph ! Corollary: Theorem 5 Let, then

39 Random Geometric Graph Geometric graph G(V,E) : Random geometric graph: If are i.i.d. in unit cube uniformly, then geometric graph is called a random geometric graph *M.Penrose, Random Geometric Graphs, Oxford University Press,2003.

40 Connectivity of Random Geometric Graph Theorem 6 Graph with is connected with probability one as if and only if ( P.Gupta, P.R.Kumar,1998 )

41 Analysis of Vicsek Model How to deal with changing neighbor graphs ? How to estimate the rate of the synchronization? How to deal with matrices with increasing dimension? How to deal with the nonlinearity of the model?

42 Dealing With Graphs With Changing Neighbors 3) Estimation of the number of agents in a ring 1)Projection onto the subspace spanned by 2) Stability analysis of TV systems (Guo, 1994)

43 Estimating the Rate of Synchronization The rate of synchronization depends on the spectral gap. Normalized Laplacian: Spectrum : Spectral gap: Rayleigh quotient

44 Lemma1: Let edges of all triangles be extracted from a complete graph. Then there exists an algorithm such that the number of residual edges at each vertex is no more than three. The Upper Bound of Lemma 2: For large n, we have =+ Example: ( G.G.Tang, L.Guo, JSSC, 2007 )

45 The Lower Bound of Lemma 3: For an undirected graph G, suppose there exists a path set P joining all pairs of vertexes such that each path in P has a length at most l, and each edge of G is contained in at most m paths in P. Then we have Lemma 4: For random geometric graphs with large n, ( G.G.Tang, L.Guo, 2007 )

46 The Lower Bound of ( G.G.Tang, L.Guo, 2007 )

47 Proposition 1: For G(n,r(n)) with large n Estimating The Spectral Gap of G(0) ( G.G.Tang, L.Guo, 2007 )

48 Analysis of Matrices with Increasing Dimension Estimation of multi-array martingales where Moreover, ifthen we have

49 Using the above corollary, we have for large n Analysis of Matrices with Increasing Dimension

50 Dealing With Inherent Nonlinearity A key Lemma: There exists a positive constantη, such that for large n, we have : with

51 For any given system parameters and when the number of agnets n is large, the Vicsek model will synchronize almost surely. Theorem 7 High Density Implies Synchronization This theorem is consistent with the simulation result.

52 Let and the velocity satisfy Then for large population, the MAS will synchronize almost surely. Theorem 8 High density with short distance interaction

53 Concluding Remarks In this lecture, we presented the synchronization analysis of the Vicsek model and the related models under deterministic framework and stochastic framework. The synchronization of three dimensional Vicsek model can be derived. There are a lot of problems deserved to be further investigated.

54 1. Deeper understanding of self-organization, What is the critical population size for synchronization with given radius and velocity ? Under random framework, dealing with the noise effect is a challenging work. How to interpret the phase transition of the model? ……

55 2. The Rule of Global Information Edges formed by the neighborhood Random connections are allowed If some sort of global interactions are exist for the agents, will that be helpful?

56 3. Other MAS beyond the Vicsek Model Nearest Neighbor Model Each node is connected with the nearest neighbors Remark: For to be asymptotically connected, neighbors are necessary and sufficient. F.Xue, P.R.Kumar, 2004

57 A birds Neighborhood Cohesion: steer to move toward the average position of neighbors Separation: steer to avoid crowding neighbors Alignment: steer towards the average heading of neighbors Boid Model: Craig Reynolds(1987):

58 Collective Behavior of Multi-Agent Systems: Intervention References: J. Han, M. Li M, L. Guo, Soft control on collective behavior of a group of autonomous agents by a shill agent, J. Systems Science and Complexity, vol.19, no.1, 54-62, Z.X. Liu, How many leaders are required for consensus? Proc. the 27th Chinese Control Conference, pp , In the next lecture, we will talk about

59 Thank you!


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