""" 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 """ 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, energy_0=1, pheromone_drop_rate_0 : dict[str, float]={"A": 80, "B": 80}, sensitivity_0=0.99, alpha=0.6, drop_pheromone=None, betas : dict[str, float]={"A": 0.0512, "B": 0.0512}, sensitivity_decay_rate=0.01, sensitivity_max = 300, sensitivity_min = 0.001, sensitivity_steepness = 1 ) -> 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 = look_for_pheromone self.drop_pheromone = drop_pheromone self.energy = energy_0 #TODO: use self.sensitivity_0 = sensitivity_0 self.sensitivity = self.sensitivity_0 self.pheromone_drop_rate = pheromone_drop_rate_0 self.alpha = alpha self.sensitivity_max = sensitivity_max self.sensitivity_min = sensitivity_min self.sensitivity_decay_rate = sensitivity_decay_rate self.sensitivity_steepness = sensitivity_steepness self.betas = betas self.threshold : dict[str, float] = {"A": 0, "B": 0} 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 For the nonlinear sensitivity, the idea is to use a logistic function that has a characteristic sigmoidal shape that starts from a low value, increases rapidly, and then gradually approaches a saturation level. f(x) = L / (1 + exp(-k*(x - x0))) f(x) = return value L = sens_max k is a parameter that controls the steepness of the curve. We can start with 1 A higher value of k leads to a steeper curve. x0 is the midpoint of the curve, where the sensitivity starts to increase significantly. We can make X0 the threshold value """ # if props iterable create array, otherwise return single value try: iter(props) except TypeError: #TODO: proper nonlinear response, not just clamping non_linear_sens = True if non_linear_sens: L = self.sensitivity_max k = self.sensitivity_steepness mid = self.threshold[key] if props > self.sensitivity_max: return self.sensitivity_max #Should we still keep these conditions? # if props > self.threshold[key]: # return props else: adjusted_sensitivity = L / (1 + np.exp(-k * (props - mid))) print(f'props: {props}, adjusted_value: {adjusted_sensitivity}') return adjusted_sensitivity else: if props > self.sensitivity_max: return self.sensitivity_max #Should we still keep these conditions if props > self.threshold[key]: return props else: return 0 arr : list[float] = [] for prop in props: arr.append(self.sens_adj(prop, key)) return np.array(arr) def _choose_next_pos(self): if self._prev_pos is None: i = np.random.choice(range(6)) 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.drop_pheromone = "B" self.look_for_pheromone = "A" self.sensitivity = self.sensitivity_0 self._prev_pos = neighbor self._next_pos = self.pos elif self.searching_nest: for neighbor in self.front_neighbors: if self.model.grid.is_nest(neighbor): self.look_for_pheromone = "A" # Is this a correct interpretation? self.drop_pheromone = "A" self.sensitivity = self.sensitivity_0 self._prev_pos = neighbor self._next_pos = self.pos # recruit new ants for agent_id in self.model.get_unique_ids(self.model.num_new_recruits): if self.model.schedule.get_agent_count() < self.model.num_max_agents: 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) # follow positive gradient if self.look_for_pheromone is not None: 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_) # TODO: if two or more neighbors have same concentration randomize? Should be unlikely with floats though index = np.argmax(gradient) if gradient[index] > 0: self._next_pos = self.front_neighbors[index] self._prev_pos = self.pos return # do biased random walk p = np.random.uniform() if p < self.alpha: self._next_pos = self.front_neighbor self._prev_pos = self.pos else: # need copy() as we would otherwise remove the tuple from all possible lists (aka python "magic") other_neighbors = self.neighbors().copy() other_neighbors.remove(self.front_neighbor) random_index = np.random.choice(range(len(other_neighbors))) self._next_pos = other_neighbors[random_index] self._prev_pos = self.pos def step(self): self.sensitivity -= self.sensitivity_decay_rate self._choose_next_pos() self._adjust_pheromone_drop_rate() #kill agent if sensitivity is low if self.sensitivity < self.sensitivity_min: self._kill_agent() def _adjust_pheromone_drop_rate(self): if(self.drop_pheromone is not None): self.pheromone_drop_rate[self.drop_pheromone] -= self.pheromone_drop_rate[self.drop_pheromone] * self.betas[self.drop_pheromone] 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[self.drop_pheromone] def _kill_agent(self): #update dead_agent list self.model.dead_agents.append(self) 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 """