AI Model for Computer games based on Case Based Reasoning and AI Planning
Making efficient AI models for games with imperfect
information can be a particular challenge. Considering the... more
Making efficient AI models for games with imperfect
information can be a particular challenge. Considering the large
number of possible moves and the incorporated uncertainties
building game trees for these games becomes very difficult due to
the exponential growth of the number of nodes at each level. This
effort is focused on presenting a method of combined Case Based
Reasoning (CBR) with AI Planning which drastically reduces the
size of game trees. Instead of looking at all possible combinations
we can focus only on the moves that lead us to specific strategies
in effect discarding meaningless moves. These strategies are
selected by finding similarities to cases in the CBR database. The
strategies are formed by a set of desired goals. The AI planning is
responsible for creating a plan to reach these goals. The plan is
basically a set of moves that brings the player to this goal. By
following these steps and not regarding the vast number of other
possible moves the model develops Game Trees which grows
slower so they can be built with more feature moves restricted by
the same amount of memory.
Download (.pdf) A Simulation Model for eLearning: Course Planning in the Participative Web
Term Project paper from Computer Science 858 at the University of Saskatchewan.
PDF on Scribd: http://www.scribd.com/doc/55965030/A-Simulation-Model-for-eLearning-Co
Description: http://blogs.usask.ca/slb534/archive/2011/05/sweet_ted_video.php
Problem Solved: Unfriendly AI
Published in h+ Magazine as the first in a series.
The so-called "Scary Idea" of an Artificial Intelligence improving itself and taking over the world assumes... more
The so-called "Scary Idea" of an Artificial Intelligence improving itself and taking over the world assumes the possibility of a logic-based intelligence that can improve without limits. But the limits on intelligence are not technological; rather, the usefulness of logic-based predictions, and hence intelligence, is limited by the unpredictability of the world. The debate about how to guarantee that future AIs will not be "Unfriendly" starts from incorrect premises and should be abandoned.
Efforts to build Logic-based Artificial intelligences are misguided. Instead, we should concentrate our efforts on Abduction based AI/AGI. These will become useful tools within our lifetimes.