| Input: Hunting game model |
| Output: Optimal hunting strategy |
| Begin |
| (Φa={θ}1,θ 2,…,θ n,Φd={θ d}); |
| //Initialize the escape action space and the hunting action space |
| (P={p}1,p2,…,pn); |
| //Initialize the escape action prior probability |
| (A={a}1,a2, …,am, D={d1,d2, …,dk}); |
| //Initialize the policy set |
| While (aj??Adh??D) //Calculate the proceeds |
| { |
| Bayes′
\left( {{\rm{\tilde p}}\left( {\theta |{\rm{a}}} \right)} \right)
|
| //Calculate the posterior probabilities |
| Ua(θ)i,aj,dh=SLC(a)j+DC(d)h,θ i-AC(a)j,θ i; |
| Ud(a)j,dh,θ i=SLC(a)j+AC(a)j,θ i-DCh-DSR(θ)i,aj,dh;} |
| for(i=1;i≤ s;i++) |
| //The s is the number of stages in the game process |
| { |
|
{\rm{d}}*\left( {\rm{a}} \right) \in {\rm{argmax}}\sum \theta = {\rm{\tilde p,}}\left( {\theta |{\rm{a}}} \right){\rm{Ud}}\left( {{\rm{a}},{\rm{d}},\theta } \right)
; |
| a*(θ)∈ maxUa(a,d)*(a),θ; |
| //Calculate the optimal escape and hunting strategies |
| Bayes′
\left( {{\rm{\tilde p}}\left( {\theta |{\rm{a}}} \right)} \right)
|
| // Use the Bayes’ rule to calculate the posterior probability of the escape action |
| Create
\left( {{\rm{d}}*\left( {\rm{a}} \right),{\rm{a}}*\left( \theta \right),{\rm{\tilde p}}\left( {\theta |{\rm{a}}} \right)} \right)
;
|
| //EQ Construct a refined Bayes’ equilibrium solution EQ |
| OutPu (td*(a)); |
| //Output the optimal hunting strategy in this stage |
| } |
| End |
