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1 function [bnet, names] = mk_bat_dbn()
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2 % MK_BAT_DBN Make the BAT DBN
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3 % [bnet, names] = mk_bat_dbn()
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4 % See
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5 % - Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a Bayesian Automated Taxi", IJCAI 95
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6 % - Boyen and Koller, "Tractable Inference for Complex Stochastic Processes", UAI98.
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7
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8 names = {'LeftClr', 'RightClr', 'LatAct', 'Xdot', 'InLane', 'FwdAct', ...
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9 'Ydot', 'Stopped', 'EngStatus', 'FBStatus', ...
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10 'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', ...
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11 'FYdotDiffSens', 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens', ...
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12 'SensorValid', 'FYdotDiff', 'FcloseSlow', 'Fclr', 'BXdot', 'BcloseFast', 'Bclr', 'BYdotDiff'};
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13 ss = length(names);
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14
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15 intrac = {...
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16 'LeftClr', 'LeftClrSens';
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17 'RightClr', 'RightClrSens';
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18 'LatAct', 'TurnSignalSens'; 'LatAct', 'Xdot';
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19 'Xdot', 'XdotSens';
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20 'FwdAct', 'Ydot';
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21 'Ydot', 'YdotSens'; 'Ydot', 'Stopped';
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22 'EngStatus', 'Ydot'; 'EngStatus', 'FYdotDiff'; 'EngStatus', 'Fclr'; 'EngStatus', 'BXdot';
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23 'SensorValid', 'XdotSens'; 'SensorValid', 'YdotSens';
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24 'FYdotDiff', 'FYdotDiffSens'; 'FYdotDiff', 'FcloseSlow';
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25 'FcloseSlow', 'FBStatus';
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26 'Fclr', 'FclrSens'; 'Fclr', 'FcloseSlow';
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27 'BXdot', 'BXdotSens';
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28 'Bclr', 'BclrSens'; 'Bclr', 'BXdot'; 'Bclr', 'BcloseFast';
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29 'BcloseFast', 'FBStatus';
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30 'BYdotDiff', 'BYdotDiffSens'; 'BYdotDiff', 'BcloseFast'};
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31 [intra, names] = mk_adj_mat(intrac, names, 1);
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32
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33
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34 interc = {...
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35 'LeftClr', 'LeftClr'; 'LeftClr', 'LatAct';
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36 'RightClr', 'RightClr'; 'RightClr', 'LatAct';
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37 'LatAct', 'LatAct'; 'LatAct', 'FwdAct';
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38 'Xdot', 'Xdot'; 'Xdot', 'InLane';
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39 'InLane', 'InLane'; 'InLane', 'LatAct';
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40 'FwdAct', 'FwdAct';
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41 'Ydot', 'Ydot';
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42 'Stopped', 'Stopped';
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43 'EngStatus', 'EngStatus';
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44 'FBStatus', 'FwdAct'; 'FBStatus', 'LatAct'};
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45 inter = mk_adj_mat(interc, names, 0);
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46
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47 obs = {'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', 'FYdotDiffSens', ...
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48 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens'};
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49
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50 for i=1:length(obs)
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51 onodes(i) = strmatch(obs{i}, names); %stringmatch(obs{i}, names);
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52 end
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53 onodes = sort(onodes);
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54
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55 dnodes = 1:ss;
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56 ns = 2*ones(1,ss); % binary nodes
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57 bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes, 'eclass2', (1:ss)+ss);
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58
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59 % make rnd params
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60 for i=1:2*ss
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61 bnet.CPD{i} = tabular_CPD(bnet, i);
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62 end
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63
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