F rank Sch w eitzer: Aktiv e Bro wnsche T eilchen: ein ph ysik
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F rank Sch w eitzer: Aktiv e Bro wnsche T eilchen: ein ph ysik
Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System zur Modellierung interaktiver Strukturbildung Frank Schweitzer http://summa.physik.hu-berlin.de/∼frank/ Institut für Physik der Humboldt-Universität zu Berlin GMD Institut für Autonome intelligente Systeme - AiS, St. Augustin Gliederung 1. Multi-Agenten-Systeme 2. Modell Aktiver Brownscher Teilchen 3. Aktive Brownsche Teilchen mit Energiedepot 4. Strukturbildung mit Aktiven Brownschen Teilchen (1) Biologische Aggregation (2) Simulation von Wegenetzen von Ameisen 5. Zusammenfassung Slide 1 $ % ' & Universität Oldenburg, 6. Juli 1999 Complex Systems Slide 2 New England Complex Systems Institute “Complex systems are systems with multiple interacting components whose behavior cannot be simply inferred from the behavior of the components. The study of complex systems spans all scales, from particle fields to the universe. Among the most complex systems with which we are familiar are biological and social systems – including biological macromolecules, biological organisms, ecosystems, and human social and economic structures.” Journal “Advances in Complex Systems” “By complex system, it is meant a system comprised of a (usually large) number of (usually strongly) interacting entities, processes, or agents, the understanding of which requires the development, or the use of, new scientific tools, nonlinear models, out-of equilibrium descriptions and computer simulations.” Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & $ % 1 agent: Multi-Agent Systems (MAS) • subunit with “intermediate” complexity ⇒ may represent local processes, individuals, species, agglomerates, components, ... • large number / different types of agents multi-agent system: • interactions between agents: – on different spatial and temporal scales – local / direct interaction – global / indirect interactions (coupling via resources) • “bottom-up approach”: no universal equations ⇒ self-organization, emergence of system properties • external influences (boundary conditions, in/outflux) ⇒ coevolution, circular causality • possibly simplest set of rules ⇒ “sufficient” complexity minimalistic agent: • cooperative interaction instead of autonomous action • no deliberative actions, no specialization, no internal memory Slide 3 $ % ' & Universität Oldenburg, 6. Juli 1999 Active Brownian Particles Slide 4 • external eigendynamics of the field • interaction between particles via multicomponent field • consideration of energetic aspects • complete stochastic dynamics, based on Langevin equations • particle-based approach to structure formation advantages: 2. discrete internal states: θi(t) – generation of different components of a self-consistent field – sensitivity to different field components → non-linear feedback ⇒ interactive structure formation on the macroscopic level 1. internal energy depot: ei(t) – take-up, storage, conversion of internal energy → pumped Brownian particles, Brownian motors internal degrees of freedom: “Active Brownian particles are Brownian particles with internal degrees of freedom. They have the ability to take up energy from the environment, to store it in an internal depot and to convert internal energy to perform different activities, such as metabolism, motion, change of the environment, or signal-response behavior.” Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & $ % 2 Α 10 Α 10 Ω 20 Ω 20 30 30 x x Observation of Brownian Motion y 30 20 10 0 y 30 20 10 0 The position of the Brownian particle (radius 0.4 µm) is documented on a millimeter grid in time intervals t0 = 30 seconds. Slide 5 $ % ' & Universität Oldenburg, 6. Juli 1999 Equations for Brownian Particles Slide 6 ξ(t): white noise, ξi(t) ξ j (t0 ) = δij δ(t − t0 ). • overdamped limit: dv i/dt = 0, or γ0 → ∞ dri √ kB T ε = 2 Dn ξ(t) ; Dn = = dt γ0 γ0 Dn: spatial diffusion coefficient of the particles fluctuation-dissipation theorem: S = kB T γ0 hF stoch (t)i = 0 ; hF stoch (t) F stoch (t0 )i = 2S δ(t − t0) F stoch: stochastic force • stochastic approach ⇒ Langevin equation: dv i dri = vi ; m = −γ0v i + F stoch dt dt γ0: friction coefficient of motion Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & $ % 3 Brownian Particle with Internal Energy Depot Depot e(t) ⇒ internal storage of energy d dt e(t) = q(r) − c e(t) − d(v) e(t) q(r): gain ⇒ space-dependent take-up of energy c: loss ⇒ internal dissipation d(v): conversion of internal into kinetic energy simple ansatz: d(v) = d2v2; d2 > 0 Stochastic equation for Brownian particles with energy depot mv̇ + γ0v + ∇U (r) = d2e(t)v + F (t) Slide 7 $ % ' & ⇒ 8.0 γ0 zero: v02 = dissipation 6.0 q0 c − γ0 d2 Universität Oldenburg, 6. Juli 1999 Non-linear Friction Function γ(v) = γ0 − d2 e(t) v 4.0 d(v) = d2v2 ; q0 c + d2 v 2 q 0 d2 c + d2 v 2 2.0 γ0−(d2q0/c) pumping γ(v) = γ0 − e0 = assumptions: q(r) ≡ q0 ; ⇒ 2.0 0.0 −2.0 −4.0 −6.0 0.0 Slide 8 ė(t) = 0 v̇ + γ0v + ∇U (r) = d2e(t)v + F (t) d e(t) = q(r) − c e(t) − d(v) e(t) dt equations of motion: Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & γ(v) $ % 4 0.0 −1.0 −2.0 σ = 1 − dc γq0 2 0 x1 1.0 2.0 σ > 0 only if: q0 > q0crit = • v approximated by the stationary velocity: q0 c − v02 = v12 + v22 = γ0 d2 ⇒ Slide 9 γ0 c d2 • efficiency ratio: dEout/dt d2 e v2 = σ= dEin /dt q0 q0 • energy depot in quasi-stationary equilibrium: e = c + d2 v 2 −1.0 Critical Supply of Energy 0.0 −1.0 −2.0 −2.0 assumption: motion in two dimensions a U (x1 , x2) = (x12 + x22) 2 q(x1 , x2) = q0 2.0 2.0 1.0 q0 = 0.0: simple Brownian motion; q0 = 1.0: motion on a stochastic limit cycle x1 1.0 0.0 2.0 −1.0 1.0 −2.0 x2 Brownian particle as micro–motor: 0.0 $ % ' & 2.0 0.0 −2.0 100 −2.0 Universität Oldenburg, 6. Juli 1999 Localized Energy Sources 200 t x1 0.0 q(x1 , x2) = 4.0 3.0 2.0 1.0 2.0 0.0 2.0 −2.0 0.0 −2.0 0 Slide 10 300 2.0 • increase in d2 abridges the cycle ⇒ directed motion more susceptible to become Brownian motion • new cycles start with a burst of energy • intermittent type of motion ⇒ oscillatory movement with fixed direction • motion into the energy area becomes accelerated • parabolic potential: U (x1 , x2) = a2 (x12 + x22) • take-up of energy in a restricted area: h i q0 if (x1 − b1)2 + (x2 − b2)2 ≤ R2 0 else Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & x2 e x2 x1 x2 $ % 5 Active Brownian Particles Responding to a Field active particles: • characterized by an internal degree of freedom: θi(t), which can be changed: w(θi0 |θi) • Langevin equation: √ dr ∂he(r, t) dv i i = vi ; = −γv i + αi + 2 εi γ ξ i(t) dt dt ∂r ri overdamped limit: v u u α ∂he(r, t) dr i i u 2εi +t = ξ (t) dt γ ∂r ri γ i he(r, t): effective field • “individual” parameters (may depend on θi) : αi: individual response to the field – attraction: αi > 0, or repulsion: αi < 0 – threshold h0: αi = Θ[he(r, t) − h0], Θ[y] = 1, if y > 0 – internal value θ: αi = δ(θi − θ) εi: individual intensity of noise – measure of the sensitivity si of the particle: si ∝ 1/εi. Slide 11 $ % ' & Universität Oldenburg, 6. Juli 1999 Effective Field N X i=1 qi(θi , t) δ(θ − θi(t)) δ(r − ri(t)) Slide 12 spatio-temporal evolution of the field, hθ (r, t): (i) decay with rate kθ (ii) diffusion (coefficient Dθ ) (iii) production with individual rate qi(θi , t) + • particles with internal parameter θ generate a field hθ (r, t) which obeys a reaction-diffusion equation: dhθ (r, t) = −kθ hθ (r, t) + Dθ ∆hθ (r, t) dt ∇he(r, t) = ∇he( . . . , hθ(r, t), hθ0 (r, t), . . . ) • effective field: a specific function of the different field components hθ (r, t): • Langevin eq.: particles respond to the gradient of he(r, t) Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & $ % 6 Complete Dynamics for N Active Particles • N active Brownian particles: internal parameters θ1, ..., θN ; positions r 1, ...., rN canonical N -particle distribution function: P (r, θ, t) = P (r 1, θ1, . . . , rN , θN , t) • changes: (i) movement: ri → ri0 (ii) transition: θi → θi0 with probability w(θi0 |θi) N X X {w(θi|θi0 )P (θi0 , θ?, r, t) • multivariate master equation: limit of strong damping: γ0 → ∞, and αi = α, εi = ε: N ∂ X P (r, θ, t) = − {∇i ((α/γ0) ∇ihe(r, t) P (θ, r, t)) ∂t i=1 −Dn ∆iP (r, θ, t)} + i=1 θi0 6=θi −w(θi0 |θi)P (θi , θ?, r, t)} • dynamics of the effective field he(r, t) Slide 13 $ % ' & C $ % δ(r − ri(t)) Universität Oldenburg, 6. Juli 1999 i=1 N X h(r,t) Examples of Circular Causation generates influences Slide 14 • track formation: bacteria, pedestrians mark their track, which can be reinforced by other individuals (ususally D0 = 0) • biological aggregation: cells, slime mold amoebae, myxobacteria generate a chemical field to communicate applications: ∇i he(r, t) = ∇i h(r, t) dh(r, t) = −k0 h(r, t) + D0 ∆hθ (r, t) + q0 dt • one-component field: • active particles are identical ⇒ no transitions αi = α > 0, εi = ε, θi = 0, qi(θi, t) = q0 = const. Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & 7 (d) (c) Aggregation of Larvae (a) (b) Aggregation of larvae of the bark beetle Dendroctonus micans. Total population 80 larvae, density 0.166 larvae/cm 2 . Time in minutes: (a) t = 0, (b) t = 5, (c) t = 10, (d) t = 20. Deneubourg, J. L.; Gregoire, J. C.; Le Fort, E.: Kinetics of Larval Gregarious Behavior in the Bark Beetle Dendroctonus micans (Coleoptera: Scolytidae), J. Insect Behavior 3/2, 169-182 (1990) Slide 15 $ % ' & Universität Oldenburg, 6. Juli 1999 (b) (a) (f) (e) (d) Aggregation of Active Brownian Particles (c) Position of 100 active Brownian particles moving on a triangular lattice (size: A = 100 × 100). Time in simulation steps: (a) t = 100, (b) t = 1.000, (c) t = 5.000, (d) t = 10.000, (e) t = 25.000, (f) t = 50.000. Slide 16 Schweitzer, F.; Schimansky-Geier, L.: Clustering of Active Walkers in a Two-Component System, Physica A 206, (1994) 359-379 Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & $ % 8 (f) (e) (d) Evolution of the Self-Consistent Field (a) (b) (c) Time in simulation steps. (left side) Growth regime: (a) t = 10, (b) t = 100, (c) t = 1.000, (right side) Competition regime: (d) t = 1.000, (e) t = 5.000, (f) t = 50.000. The scale of the right side is 10 times the scale of the left side. Hence, Fig. (d) is the same as Fig. (c). Schweitzer, F.; Schimansky-Geier, L.: Clustering of Active Walkers in a Two-Component System, Physica A 206, (1994) 359-379 Slide 17 $ % ' & Universität Oldenburg, 6. Juli 1999 generates influences h-1(r,t) h+1(r,t) influences generates C+1 Examples of Circular Causation C-1 applications: • exploitation of food sources: ants mark trails from food sources to the nest with additional chemicals to guide nestmates to resources • self-assembling of networks: particles responding to two different fields, link nodes with opposite potential Slide 18 $ % • state dependent production rate θi qi(θi, t) = [(1 + θi) qi (+1, t) − (1 − θi) qi(−1, t)−1] 2 • two-component field θi ∇i he(r, t) = [(1 + θi) ∇i h−1(r, t) − (1 − θi) ∇i h+1(r, t)] 2 N dhθ (r, t) X = −kθ hθ (r, t) + qi(θi, t) δ(θ − θi(t)) δ(r − ri(t)) dt i=1 • active particles with two different states θ ∈ {−1, +1}, transitions possible Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & 9 Slide 19 Hölldobler, B. and Möglich, M.: The foraging system of Pheidole militicida (Hymenoptera: Formicidae), Insectes Sociaux 27/3 (1980) 237-264 Schematic representation of the complete foraging route of Pheidole milicida, a harvesting ant of the southwestern U.S. deserts. Each day tens of thousands of workers move out to the dendritic trail system, disperse singly, and forage for food. Foraging Route of Ants (Pheidole milicida) $ % ' & Universität Oldenburg, 6. Juli 1999 b a f e d Slide 20 Schweitzer, F.; Lao, K.; Family, F.: Active Random Walkers Simulate Trunk Trail Formation by Ants, BioSystems 41 (1997) 153-166 Formation of trails from a nest (middle) to a line of food at the top and the bottom of a lattice. (a-c) show the distribution of chemical component (+1), and (d-f) show the distribution of chemical component (−1). Time in simulation steps: (a), (d) t = 1.000, (b), (e) t = 5.000, (c), (f) t = 10.000. c Trunk Trail Formation to Extended Forage Areas Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & $ % 10 d c Trunk Trail Formation to Separate Food Items a b Formation of trails from a nest (middle) to five randomly placed food clusters. The distribution of chemical component (−1) is shown after (a) 2000, (b) 4000, (c) 8500, and (d) 15000 simulation time steps, respectively. Schweitzer, F.; Lao, K.; Family, F.: Active Random Walkers Simulate Trunk Trail Formation by Ants, BioSystems 41 (1997) 153-166 Slide 21 $ % ' & Universität Oldenburg, 6. Juli 1999 t = 50 Formation of Networks t = 10 t = 200 t = 10.000 t = 100 t = 1.000 Slide 22 Time series of the evolution of a network (time in simulation steps). Lattice size 100 × 100, 5.000 particles, 40 nodes, z+ = 20, z− = 20. Parameters: q0 = 10.000, kh = 0.03, β = 0.2. Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' & $ % 11 $ % ' & Universität Oldenburg, 6. Juli 1999 Self-Organization Slide 24 SFB 230 “Natural Constructions”, Stuttgart, 1984 - 1995 Self-organization is defined as spontaneous formation, evolution and differentiation of complex order structures forming in non-linear dynamic systems by way of feedback mechanisms involving the elements of the systems, when these systems have passed a critical distance from the statical equilibrium as a result of the influx of unspecific energy, matter or information. Physico-Chemical and Life Sciences, EU Report 16546 (1995) Biebricher, C. K.; Nicolis, G.; Schuster, P.: Self-Organization in the Self-organization is the process by which individual subunits achieve, through their cooperative interactions, states characterized by new, emergent properties transcending the properties of their constitutive parts. Frank Schweitzer: Aktive Brownsche Teilchen: ein physikalisches Multi-Agenten-System ' Conclusions • particle-based model for interactive structure formation 1. Model of Active Brownian Particles: • relation to biology: (a) energy consumption for metabolism and motion (b) interaction with the environment due to a self-consistent multicomponent field ⇒ non-linear feedback 2. Conversion of Brownian Motion into Directed Motion: (a) quasiperiodic movement of the particles between energy sources and “home” Slide 23 • applicable to systems where only small particle numbers govern the system dynamics • efficient and stable simulation algorithm: instead of integrating PDE ⇒ simulation of the Langevin equation • stochastic approach to directed movement and structure formation 3. Advantage of the Active Brownian Particles Model: (c) chemotactic response to a self-consistent chemical field two-component field ⇒ directed forward / backward motion (b) directed movement in a asymetric periodic potential ⇒ direction depends on conversion parameter d2 and noise D & $ % 12