### Table 1. Results of implementation of the Pursuit problem usingsimple BFS to various levels (i.e. regular RMM), using our Limited Rationality Recursive Modeling algorithm, and using a simple greedy algorithm, in which each predator simply tries to minimize its distance to the prey.

1996

"... In PAGE 13: ... This was done by generating random situations and running RMM to 3 or 4 levels on them to find out the strategy. The results, as seen in Table1 , show that our algorithm managed to maintain the performance of a BFS to 4 levels while only expanding little more than four nodes on the average. All the results are the averages of 20 or more runs.... ..."

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### Table 4.1: Results of implementation of the Pursuit problem using simple BFS to various levels (i.e., regular RMM), using our Limited Rationality Recursive Modeling algorithm, and using a simple greedy algorithm, in which each predator simply tries to mini- mize its distance to the prey.

### Table 2. Experimental results for some problem instances without H-boxes The pre-computation of the node order proved quite useful. If the nodes are sorted by their weighted degrees only, the problem of calculating the upper bound remains: One has to scan all free nodes whether there still are nodes of a certain type left or not. If the nodes are sorted by their type, this examination is obsolete: If the algorithm adds a node of type t, then it is guaranteed that no more nodes of previous types are free. This leads to an additional time saving effect. The runtimes needed for verifying the optimal solution are compared in Table 3. Within this context, verifying an optimal solution implies a complete run of the algorithm. The algorithms timestamp, randomized and sorted refer to algo- rithm 2 and use different node orders: the time of creation, a randomized order and sorted by box size and weighted degree, respectively. Algorithm greedy refers to the recursive greedy algorithm discussed in section 2.7.

"... In PAGE 12: ... 4 Experimental results All grid based algorithms discussed so far are exact, so they will find the optimal solution for the given discretization of the trunk. Table2 shows the best achieved results of some typical instances. As can be seen, the results achieved by the LP approach are comparable to those provided by the grids.... In PAGE 13: ... Otherwise, the algorithm was stopped after 24 hours runtime. Table2 shows that both approaches, the grid-based and the LP-based approach, are almost equal with slight quality advantage on the LP side. On the other hand, the grid-based algorithms are easier to operate.... ..."

### Table 1: Results of implementation of the Pursuit prob- lem using simple BFS to various levels (i.e. regular RMM), using our Limited Rationality Recursive Modeling algo- rithm, and using a simple greedy algorithm, in which each predator simply tries to minimize its distance to the prey.

"... In PAGE 7: ... This was done by generating random situations and running RMM to 3 or 4 levels on them to find out the strategy. The results, as seen in Table1 , show that our algorithm managed to maintain the perfor- mance of a BFS to 4 levels while only expanding little more than four nodes on the average. All the results are the averages of 20 or more runs.... ..."

### Table 1: The Greedy algorithm.

2005

"... In PAGE 18: ...dditional lines of pseudocode for iGreedyLB. Lines 5.1, 9.1 and 13.1-13.4 are new and should be added to the pseudocode of Table1 after lines 5, 9 and 13, while lines 7 and 10 are substitutes for the corresponding lines of Table 1.... In PAGE 18: ...dditional lines of pseudocode for iGreedyLB. Lines 5.1, 9.1 and 13.1-13.4 are new and should be added to the pseudocode of Table 1 after lines 5, 9 and 13, while lines 7 and 10 are substitutes for the corresponding lines of Table1 .... ..."

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### Table 1. The greedy algorithm

2006

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### Table 1: Backward recursive algorithm

2000

"... In PAGE 6: ... Since the cost functional is continuously di eren- tiable and strictly convex, Q M (k;;n) is also a convex problem with linear constraints and has a unique solu- tion at a nite point. The backward recursive algorithm for solving the optimal control problem for multiple fur- nace system, P M , can be described as in ALGO 1 and ALGO 2 in Table1 . We omit the detailed algorithm here.... ..."

Cited by 2