Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
In problem solving, there is a search for the appropriate
solution. A state space is a problem domain consisting of
the start state, the goal state and the operations that will
necessitate the various moves from the start state to the goal
state. Each move operation takes one away from the start
state and closer to the goal state. In this work we have
attempted implementing this concept in adversarial problem
solving, which is a more complex problem space. We noted
that real world adversarial problems vary in their types and
complexities, and therefore solving an adversarial problem
would depend on the nature of the adversarial problem
itself. Specifically, we examined a real world case, “the
prisoner’s dilemma” which is a critical, mutually
independent, decision making adversarial problem. We
combined the idea of the Thagard’s Theory of Explanatory
Coherence (TEC) with Bayes’ theorem of conditional
probability to construct the model of an opponent that
includes the opponent’s model of the agent. A further
conversion of the model into a series of state space
structures led us into the use of breadth-first search strategy
to arrive at our decision goal.
