This paper reexamines the game-theoretic bargaining theory from

logic and Artificial Intelligence perspectives. We present an

axiomatic characterization of the logical solutions to bargaining

problems. A bargaining situation is described in propositional logic

with numerical representation of bargainers' preferences. A solution

to the n-person bargaining problems is proposed based on the

maxmin rule over the degrees of bargainers' satisfaction. The

solution is uniquely characterized by four axioms collective

rationality, scale invariance, symmetry and

mutually comparable monotonicity in conjunction with three

other fundamental assumptions individual rationality,

consistency and comprehensiveness. The Pareto

efficient solutions are characterized by the axioms scale

invariance, Pareto optimality and restricted mutually

comparable monotonicity along with the basic assumptions. The

relationships of these axioms and assumptions and their links to

belief revision postulates and game theory axioms are discussed. The

framework would help us to identify the logical reasoning behind

bargaining processes and would initiate a new methodology of

bargaining analysis.