/// <summary>
/// Determine the agent's position in the world relative to the prey and walls, and set its sensor inputs accordingly.
/// </summary>
/// <param name="agent"></param>
public void SetAgentInputsAndActivate(IBlackBox agent)
{
// Calc prey's position relative to the agent (in polar coordinate system).
PolarPoint relPos = CartesianToPolar(_preyPos - _agentPos);
// Determine agent sensor input values.
// Reset all inputs.
agent.InputSignalArray.Reset();
// Test if prey is in sensor range.
if (relPos.RadialSquared <= _sensorRangeSquared)
{
// Determine which sensor segment the prey is within - [0,7]. There are eight segments and they are tilted 22.5 degrees (half a segment)
// such that due North, East South and West are each in the centre of a sensor segment (rather than on a segment boundary).
double thetaAdjusted = relPos.Theta - PiDiv8;
if (thetaAdjusted < 0.0)
{
thetaAdjusted += 2.0 * Math.PI;
}
int segmentIdx = (int)Math.Floor(thetaAdjusted / PiDiv4);
// Set sensor segment's input.
agent.InputSignalArray[segmentIdx] = 1.0;
}
// Prey closeness detector.
agent.InputSignalArray[8] = relPos.RadialSquared <= 4.0 ? 1.0 : 0.0;
// Wall detectors - N,E,S,W.
// North.
int d = (_gridSize - 1) - _agentPos._y;
if (d <= 4)
{
agent.InputSignalArray[9] = (4 - d) / 4.0;
}
// East.
d = (_gridSize - 1) - _agentPos._x;
if (d <= 4)
{
agent.InputSignalArray[10] = (4 - d) / 4.0;
}
// South.
if (_agentPos._y <= 4)
{
agent.InputSignalArray[11] = (4 - _agentPos._y) / 4.0;
}
// West.
if (_agentPos._x <= 4)
{
agent.InputSignalArray[12] = (4 - _agentPos._x) / 4.0;
}
// Activate agent.
agent.Activate();
}
/// <summary>
/// Move the prey. The prey moves by a simple set of stochastic rules that make it more likely to move away from
/// the agent, and more so when it is close.
/// </summary>
public void MovePrey()
{
// Determine if prey will move in this timestep. (Speed is simulated stochastically)
if (_rng.NextDouble() > _preySpeed)
{
return;
}
// Determine position of agent relative to prey.
PolarPoint relPolarPos = CartesianToPolar(_agentPos - _preyPos);
// Calculate probabilities of moving in each of the four directions. This stochastic strategy is taken from:
// Incremental Evolution Of Complex General Behavior, Faustino Gomez and Risto Miikkulainen (1997)
// (http://nn.cs.utexas.edu/downloads/papers/gomez.adaptive-behavior.pdf)
// Essentially the prey moves randomly but we bias the movements so the prey moves away from the agent, and thus
// generally avoids getting eaten through stupidity.
double t = T(Math.Sqrt(relPolarPos.RadialSquared));
double[] probs = new double[4];
probs[0] = Math.Exp(W(relPolarPos.Theta, Math.PI / 2.0) * t * 0.33); // North.
probs[1] = Math.Exp(W(relPolarPos.Theta, 0) * t * 0.33); // East.
probs[2] = Math.Exp(W(relPolarPos.Theta, Math.PI * 1.5) * t * 0.33); // South.
probs[3] = Math.Exp(W(relPolarPos.Theta, Math.PI) * t * 0.33); // West.
DiscreteDistribution dist = new DiscreteDistribution(probs);
int action = DiscreteDistribution.Sample(_rng, dist);
switch (action)
{
case 0: // Move north.
if (_preyPos._y < _gridSize - 1)
{
_preyPos._y++;
}
break;
case 1: // Move east.
if (_preyPos._x < _gridSize - 1)
{
_preyPos._x++;
}
break;
case 2: // Move south.
if (_preyPos._y > 0)
{
_preyPos._y--;
}
break;
case 3: // Move west.
if (_preyPos._x > 0)
{
_preyPos._x--;
}
break;
}
}
/// <summary>
/// Move the prey. The prey moves by a simple set of stochastic rules that make it more likely to move away from
/// the agent, and more so when it is close.
/// </summary>
public void MovePrey()
{
// Determine if prey will move in this timestep. (Speed is simulated stochastically)
if (_rng.NextDouble() > _preySpeed)
{
return;
}
// Determine position of agent relative to prey.
PolarPoint relPolarPos = PolarPoint.FromCartesian(_agentPos - _preyPos);
// Calculate probabilities of moving in each of the four directions. This stochastic strategy is taken from:
// Incremental Evolution Of Complex General Behavior, Faustino Gomez and Risto Miikkulainen (1997)
// (http://nn.cs.utexas.edu/downloads/papers/gomez.adaptive-behavior.pdf)
// Essentially the prey moves randomly but we bias the movements so the prey moves away from the agent, and thus
// generally avoids getting eaten through stupidity.
double T = MovePrey_T(relPolarPos.Radial);
double[] probs = new double[4];
probs[0] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, Math.PI / 2.0) / Math.PI) * T * 0.33); // North.
probs[1] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, 0) / Math.PI) * T * 0.33); // East.
probs[2] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, Math.PI * 1.5) / Math.PI) * T * 0.33); // South.
probs[3] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, Math.PI) / Math.PI) * T * 0.33); // West.
DiscreteDistribution rwl = new DiscreteDistribution(probs);
int action = DiscreteDistributionUtils.Sample(rwl, _rng);
switch (action)
{
case 0: // Move north.
_preyPos._y = Math.Min(_preyPos._y + 1, _gridSize - 1);
break;
case 1: // Move east.
_preyPos._x = Math.Min(_preyPos._x + 1, _gridSize - 1);
break;
case 2: // Move south.
_preyPos._y = Math.Max(_preyPos._y - 1, 0);
break;
case 3: // Move west (is the best?)
_preyPos._x = Math.Max(_preyPos._x - 1, 0);
break;
}
}