""" agent.py - Part of ants project This model implements the actual agents on the grid (a.k.a. the ants) License: AGPL 3 (see end of file) (C) Alexander Bocken, Viviane Fahrni, Grace Kagho """ """ TO DISCUSS: Is the separation of energy and sensitivity useful? -> only if we have the disconnect via resistance """ import numpy as np import numpy.typing as npt from mesa.agent import Agent from mesa.space import Coordinate class RandomWalkerAnt(Agent): def __init__(self, unique_id, model, look_for_pheromone=None, drop_pheromone=None, sensitivity_max = 10000, ) -> None: super().__init__(unique_id=unique_id, model=model) self._next_pos : None | Coordinate = None self._prev_pos : None | Coordinate = None self.look_for_pheromone : str|None = look_for_pheromone self.drop_pheromone : str|None = drop_pheromone self.energy : float = self.model.e_0 self.sensitivity : float = self.model.s_0 self.pheromone_drop_rate : float = self.model.q_0 self.sensitivity_max = sensitivity_max def sens_adj(self, props, key) -> npt.NDArray[np.float_] | float: """ returns the adjusted value of any property dependent on the current sensitivity. The idea is to have a nonlinear response, where any opinion below a threshold (here: self.threshold[key]) is ignored, otherwise it returns the property Long-term this function should be adjusted to return the property up to a upper threshold as well. returns ^ | sens_max| __________ | / | / q^tr| / | 0|________ -----------------------> prop """ # if props iterable create array, otherwise return single value try: iter(props) except TypeError: # TODO: proper nonlinear response, not just clamping if props > self.sensitivity_max: return self.sensitivity_max if props > self.model.q_tr: return props else: return 0 arr : list[float] = [] for prop in props: arr.append(self.sens_adj(prop, key)) return np.array(arr) def _get_resistance_weights(self, positions=None): if positions is None: positions = self.neighbors() # bit round-about but self.model.grid.fields['res'][positions] # gets interpreted as slices, not multiple singular positions resistance = np.array([ self.model.grid.fields['res'][x,y] for x,y in positions ]) easiness = np.max(self.model.grid.fields['res']) - resistance + np.min(self.model.grid.fields['res']) + 1e-15 # + epsilon to not divide by zero weights = easiness/ np.sum(easiness) #inv_weights = resistance/ np.sum(resistance) #weights = 1 - inv_weights #weights /= np.sum(weights) return weights def _choose_next_pos(self): def _combine_weights(res_weights, walk_weights): """ If we have a resistance -> Infinity we want to have a likelihood -> 0 for this direction Therefore we should multiply our two probabilities. For the case of no resistance field this will return the normal walk_weights res_weights : resistance weights: based on resistance field of neighbours see _get_resistance_weights for more info walk weights: In case of biased random walk (no positive pheromone gradient): forward: alpha, everywhere else: (1- alpaha)/5) In case of positive pheromone gradient present in front: max. positive gradient: self.sensitivity everyhwere else: (1-self.sensitivity)/5 """ combined = res_weights * walk_weights normalized = combined / np.sum(combined) return normalized def _pick_from_remaining_five(remaining_five): """ """ weights = self._get_resistance_weights(remaining_five) random_index = np.random.choice(range(len(remaining_five)), p=weights) self._next_pos = remaining_five[random_index] self._prev_pos = self.pos if self._prev_pos is None: res_weights = self._get_resistance_weights() walk_weights = np.ones(6) weights = _combine_weights(res_weights, walk_weights) i = np.random.choice(range(6),p=weights) assert(self.pos is not self.neighbors()[i]) self._next_pos = self.neighbors()[i] self._prev_pos = self.pos return if self.searching_food: for neighbor in self.front_neighbors: if self.model.grid.is_food(neighbor): self.model.grid.fields['food'][neighbor] -= 1 # eat #resets self.pheromone_drop_rate = self.model.q_0 self.sensitivity = self.model.s_0 self.energy = self.model.e_0 #now look for other pheromone self.look_for_pheromone = "A" self.drop_pheromone = "B" self._prev_pos = neighbor self._next_pos = self.pos return elif self.searching_nest: for neighbor in self.front_neighbors: if self.model.grid.is_nest(neighbor): #resets self.pheromone_drop_rate = self.model.q_0 self.sensitivity = self.model.s_0 self.energy = self.model.e_0 self.look_for_pheromone = "B" self.drop_pheromone = "A" self._prev_pos = neighbor self._next_pos = self.pos self.model.successful_ants += 1 # recruit new ants print("RECRUITING") for agent_id in self.model.get_unique_ids(self.model.N_r): if self.model.schedule.get_agent_count() < self.model.N_m: agent = RandomWalkerAnt(unique_id=agent_id, model=self.model, look_for_pheromone="B", drop_pheromone="A") agent._next_pos = self.pos self.model.schedule.add(agent) self.model.grid.place_agent(agent, pos=neighbor) return # follow positive gradient with likelihood self.sensitivity if self.look_for_pheromone is not None: # Calculate gradient front_concentration = [self.model.grid.fields[self.look_for_pheromone][cell] for cell in self.front_neighbors ] front_concentration = self.sens_adj(front_concentration, self.look_for_pheromone) current_pos_concentration = self.sens_adj(self.model.grid.fields[self.look_for_pheromone][self.pos], self.look_for_pheromone) gradient = front_concentration - np.repeat(current_pos_concentration, 3).astype(np.float_) index = np.argmax(gradient) if gradient[index] > 0: # follow positive gradient with likelihood self.sensitivity * resistance_weight (re-normalized) all_neighbors_cells = self.neighbors() highest_gradient_cell = self.front_neighbors[index] highest_gradient_index_arr = np.where(all_neighbors_cells == highest_gradient_cell) assert(len(highest_gradient_index_arr) == 1) all_neighbors_index = highest_gradient_index_arr[0] sens_weights = np.ones(6) * (1-self.sensitivity)/5 sens_weights[all_neighbors_index] = self.sensitivity res_weights = self._get_resistance_weights() weights = _combine_weights(res_weights, sens_weights) random_index = np.random.choice(range(6), p=weights) self._next_pos = all_neighbors_cells[random_index] self._prev_pos = self.pos return # do biased random walk all_neighbors_cells = self.neighbors() front_index_arr = np.where(all_neighbors_cells == self.front_neighbor) assert(len(front_index_arr) == 1 ) front_index = front_index_arr[0] res_weights = self._get_resistance_weights() walk_weights = np.ones(6) * (1-self.model.alpha) / 5 walk_weights[front_index] = self.model.alpha weights = _combine_weights(res_weights, walk_weights) random_index = np.random.choice(range(6), p=weights) self._next_pos = all_neighbors_cells[random_index] self._prev_pos = self.pos def step(self): # Die and get removed if no energy if self.energy < self.model.e_min: self.model.schedule.remove(self) #update list of dead agents for time step self.model.dying_agents += 1 else: self._choose_next_pos() self._adjust_pheromone_drop_rate() self.sensitivity -= self.model.d_s self.energy -= self.model.grid.fields['res'][self.pos] * self.model.d_e def _adjust_pheromone_drop_rate(self): if(self.drop_pheromone is not None): self.pheromone_drop_rate -= self.pheromone_drop_rate * self.model.beta def drop_pheromones(self) -> None: # should only be called in advance() as we do not use hidden fields if self.drop_pheromone is not None: self.model.grid.fields[self.drop_pheromone][self.pos] += self.pheromone_drop_rate def advance(self) -> None: self.drop_pheromones() self.model.grid.move_agent(self, self._next_pos) self._next_pos = None # so that we rather crash than use wrong data # TODO: find out how to decorate with property properly def neighbors(self, pos=None, include_center=False): if pos is None: pos = self.pos return self.model.grid.get_neighborhood(pos, include_center=include_center) @property def searching_nest(self) -> bool: return self.drop_pheromone == "B" @property def searching_food(self) -> bool: return self.drop_pheromone == "A" @property def front_neighbors(self): """ returns all three neighbors which the ant can see """ all_neighbors = self.neighbors() neighbors_at_the_back = self.neighbors(pos=self._prev_pos, include_center=True) front_neighbors = list(filter(lambda i: i not in neighbors_at_the_back, all_neighbors)) ########## DEBUG try: assert(self._prev_pos is not None) assert(self._prev_pos is not self.pos) assert(self._prev_pos in all_neighbors) assert(len(front_neighbors) == 3) except AssertionError: print(f"{self._prev_pos=}") print(f"{self.pos=}") print(f"{all_neighbors=}") print(f"{neighbors_at_the_back=}") print(f"{front_neighbors=}") raise AssertionError else: return front_neighbors @property def front_neighbor(self): """ returns neighbor of current pos which is towards the front of the ant """ neighbors__prev_pos = self.neighbors(self._prev_pos) for candidate in self.front_neighbors: # neighbor in front direction only shares current pos as neighborhood with _prev_pos candidate_neighbors = self.model.grid.get_neighborhood(candidate) overlap = [x for x in candidate_neighbors if x in neighbors__prev_pos] if len(overlap) == 1: return candidate """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, version 3. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see """