hub: repo_type: generator builtin: metabolic-robot-controller tags: - banzhaf-yamamoto chemistries: - id: metabolic-robot-controller name: Metabolic / artificial biochemical network robot controllers intuition: 'A reaction network used as a brain. Sensors feed substances into the network, the network''s own reactions process them, and a motor consumes the concentration of a designated actuator species - so the robot''s behaviour *is* the network''s dynamics. Hand-designed networks do light-seeking and obstacle avoidance; evolved ones transfer from simulation to real hardware with little more than a scaling factor. The appeal is that computation and its substrate are not separable here. ' origin: Ziegler & Banzhaf, 2001; Lones, Fuente, Turner et al. book: 16.1.3 refs: - '[958]' - '[959]' - '[519]' - '[520]' - '[68]' - '[660]' family: application kind: generator constructive: false S: repr: abstract substances s1, s2, ...; sensor substances (b, c, d, e) receive sensor-driven inflow, the actuator substance a is read and consumed by the motor definition: explicit notes: 'The GP system that evolves these graphs is constructive (mutation and crossover add and remove molecules), but a single reaction graph has a fixed, enumerable species set. ' R: definition: explicit arity: - 1 - 2 scheme: 'Bipartite directed weighted graph G = (V, E), V = R u S. Reaction nodes have the types s1 -> s1'' ; s1 + s2 -> s1'' ; s1 -> s1'' + s2 ; s1 + s2 -> s1'' + s2'' (paper eqs. 4-7). Stoichiometric edges carry the default weight k_0; a special edge S -> R carries a catalytic or inhibitory weight k in [k_min, k_max]. A catalysed node becomes X -> Y [k_0] plus X + C -> Y + C [k_0 k]. The graph must fulfil material balance: positive species weights m with M(R) m = 0. ' A: reactor: - lattice-2d - ode dilution: sensor substances flow in (eq. 30); the actuator substance is consumed at rate alpha above a_min (eq. 31); the 2001 lattice reactor also removes random molecules notes: 'Ziegler & Banzhaf (2001) simulate the graph in a 20 x 20 toroidal lattice with Moore neighbourhood; Ziegler, Dittrich & Banzhaf (1998) use a one-compartment ODE reactor with the same mass-action form for catalysis. ' params: - name: n_substances type: int default: 8 min: 2 max: 500 range: must include the input and output substances; the evolved graph of the paper's fig. 13 has nearly 50 nodes (molecules + reactions) and 70 edges role: structural meaning: number of molecule nodes |S|, including the sensor and actuator substances; the others are named s1, s2, ... (the paper numbers them; the prefix keeps '2 s2' readable) - name: n_reactions type: int default: 8 min: 0 max: 1000 role: structural meaning: number of reaction nodes |R|, each kept only if the graph stays materially balanced - name: reaction_types type: list default: - 4 - 5 - 6 - 7 role: structural meaning: 'allowed reaction types by the paper''s equation number: 4 s1 -> s1'', 5 s1 + s2 -> s1'', 6 s1 -> s1'' + s2, 7 s1 + s2 -> s1'' + s2'' (Table 1 uses all four)' - name: p_modifier type: float default: 0.5 min: 0 max: 1 role: structural meaning: probability that a reaction node carries a catalytic or inhibitory edge (at most one per node, as in mutation case 1); not published - name: p_inhibitor type: float default: 0.5 min: 0 max: 1 role: structural meaning: probability that a modifier edge is inhibitory rather than catalytic; not published - name: k_0 type: float default: 1.0 min: 0 role: kinetic meaning: default weight k_default of stoichiometric edges, the rate constant of the spontaneous reaction (1.0 in Ziegler, Dittrich & Banzhaf 1998) - name: k_min type: float default: 10.0 min: 0 role: kinetic meaning: lower bound of catalytic/inhibitory weights, drawn uniformly in [k_min, k_max] - name: k_max type: float default: 10.0 min: 0 range: the 2001 paper gives no values; the default k_min = k_max = 10 is the catalytic efficiency kappa of the 1998 paper role: kinetic meaning: upper bound of catalytic/inhibitory weights - name: sensor_map type: dict default: b: - left1 - left2 c: - front1 - front2 d: - right1 - right2 e: - back1 - back2 role: structural meaning: 'input set I: each sensor substance and the Khepera proximity sensors whose maximum drives its inflow (eq. 29)' - name: sensor_readings type: dict default: left1: 1023 left2: 1023 front1: 1023 front2: 1023 right1: 1023 right2: 1023 back1: 1023 back2: 1023 role: population meaning: current sensor values in [0, 1023] by sensor name; unlisted sensors read 0. The default is the paper's robot surrounded by obstacles - name: max_inflow type: float default: 0.1 min: 0 max: 1 role: population meaning: 'maxInflow of eq. 30: inflow of a sensor substance at maximum reading, as a fraction of the reactor volume per iteration (10% in the paper)' - name: actuator_map type: dict default: a: rotate role: structural meaning: 'output set O: each actuator substance and its action, one of rotate (2001: switch one motor''s direction above a_min), left-motor, right-motor (1998: substance drives a wheel)' - name: alpha type: float default: 0.9 min: 0 max: 1 role: population meaning: 'consumption rate alpha of the actuator substance while it is above a_min (Table 2: 0.9)' - name: a_min type: float default: 0.1 min: 0 role: population meaning: 'actuator activation threshold a_min (Table 2: 0.1)' provides: - topology - stoichiometry - catalysts - rate-constants - mass-conservation - flow fidelity: reconstructed sources: - Ziegler, J. & Banzhaf, W. (2001). Evolving control metabolisms for a robot. Artificial Life 7(2):171-190. Reaction graphs sec. 3 (eqs. 4-17), material balance sec. 3.1.1, mutation sec. 3.2.1, sensors and actuators sec. 5.1-5.2 (eqs. 29-31), Tables 1-2. http://www.cs.mun.ca/~banzhaf/papers/metabolism.pdf - 'Ziegler, J., Dittrich, P. & Banzhaf, W. (1998). Towards a metabolic robot control system. In Holcombe & Paton (eds.), Information Processing in Cells and Tissues, pp. 305-318. Appendix, example A: mass-action form of catalysed reactions and k = 1.0, kappa = 10. https://users.fmi.uni-jena.de/~dittrich/p/ZDB97ipcat.ps' decisions: - 'The network is one individual of the GP system: a random reaction graph. The (mu, lambda) evolution, mutation, crossover, pathway extraction and the robot simulation act on many networks and are not generated; the v1 evolution parameter is dropped.' - Reaction nodes are drawn like mutation case 4 (type uniform over reaction_types, participants uniform over all molecules, with replacement). No-op reactions and duplicate nodes are rejected. The paper's 'grow' initialisation is not described further. - Material balance (sec. 3.1.1) is tested as the existence of weights m >= 1 with M(R) m = 0, by linear programming. The paper's steps 4-6 set the independent weights to unity and check the rest, which can wrongly reject a balanced network whose positive solution needs other values. A node that would break the balance is rejected rather than penalised. Once the weight vector is unique, a node is kept iff it is orthogonal to it. - 'Extraction garbling: eqs. 4-7 lost their primes (printed ''s1 -> s1''); they are read as s1 -> s1'', etc., since the unprimed forms would be no-ops.' - 'Catalysis: the 2001 paper only says a catalyst multiplies the rate constant by a factor. The 1998 appendix writes a catalysed reaction as a parallel mass-action channel w5 = k5 kappa [A][B][B] with k5 = k1, so a catalysed node becomes the plain reaction (k_0) plus a channel with the catalyst on both sides (k_0 k). The kappa glyph is lost in the extracted text and is restored from the table ''kappa 10''.' - Inhibition has no published functional form. An inhibited node is one mass-action reaction with k_0 whose rate dict records inhibitor and k_inhibition; a simulator must choose the inhibitory factor. - 'Modifier edges: at most one per node, as mutation case 1 implies (''adding an inhibitor/catalyst if none is present''). The modifier is any molecule, with k ~ U[k_min, k_max]. p_modifier and p_inhibitor are not in the paper.' - 'Rate defaults: the 2001 paper gives no k_default, k_min or k_max. The defaults k_0 = 1.0 and k_min = k_max = 10 are the 1998 paper''s k_i and kappa.' - Sensor inflow (eq. 30) is expressed per reactor iteration as a fraction of the reactor volume (cellsize = 1), so max_inflow = 0.1 gives 0.4 in total when all four sensor pairs read 1023, as the paper states. sensor_map and actuator_map replace the v1 callables. - 'Actuator outflow (eq. 31) is recorded as outflow alpha. The threshold gate [a] > a_min is not a rate law and is kept in extras.actuators. Reactor size, fill level and cycle counts (Table 2) only size the simulation and are dropped. No initial state is set: the paper fills 50% of the lattice without giving the composition.' - The functional set (kernel, eq. 16) and pathways P(v, w) are analyses of a graph, not part of it, and are not computed. extras.reaction_graph lists each reaction node with its type, modifier and the indices of the network reactions that implement it. phenomena: - light seeking and obstacle avoidance on a Khepera robot (1998, hand-designed enzyme-substrate network) - evolved networks transfer between simulation and a real Khepera, with only a scale factor on sensor inflow - the evolved kernel uses only the front and left sensor substances to raise the motor substance (fig. 14) - genome length grows during evolution (introns, bloat) while the kernel stays small - combined genetic + metabolic + signalling networks controlling chaotic dynamical systems (Lones et al.) notes: 'The paper also defines a shortest-path ''metabolic pathway'' extraction, the subgraph P(v, w) with every participant of each reaction on the path, used by crossover. It would fit as a Chemart analysis on extras.reaction_graph. '