By Driessen T.
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Extra resources for A bankruptcy problem and an information trading problem: Applications to k-convex games
None of the reviewers caught on to either cheat, and in fact many of the intelligent behaviors they ascribed to the AI were, as suggested earlier, really just side effects of the ways in which we cheated. 3 Conclusion Academic AI can be about a great many things. It can be about solving hard problems, recreating human intelligence, modeling human cognition in order to learn more about how our brain works, optimizing performance in complex operational environments (for instance, for an autonomous robot), or any of a host of other extremely challenging and worthwhile pursuits.
First, when exploring, we gave the AI a random chance every 30 seconds or so to cheat and explore an area where we knew there was something good to find. This helped us to avoid games where the AI just didn’t happen to find any of the nearby resources early enough in the game. The second was to keep track of the approximate amount of enemy strength in an area (but not the specific locations of units). This allowed us to assign reasonable amounts of strength to our attack and defend goals, and to avoid sending units off on a wild goose chase.
Most of the general tweaks for making influence maps work [Champandard 11] 2. 1 Diagrammatic representation of a neuron at rest. Four ions are represented, potassium (K+), sodium (Na+), chlorine (Cl–), and an electrically charged anion (A–) representing intra cellular proteins. Notice here that total charge on either side of the cell is net zero and that the number of counted ions is balanced at 16 on each side. can be, in actual fact, worked into the diffusion equation. In textbooks, the equation has a tendency to look formidable: ∂c ( r , t ) = ∇ .