To make artificial creatures deliberately interact with their environment like living creatures, a behavior selection method mimicking living creatures' thought mechanism is needed. For this purpose, there has been research based on probabilistic knowledge links between input (assumed fact) and target (behavior) symbols for reasoning. However, real intelligent creatures including human beings select a behavior based on the multi-criteria decision making process using the degree of consideration (DoC) for input symbols, i.e. will and context symbols, in their memory. In this paper, the DoC-based mechanism of thought (DoC-MoT) is proposed and applied to the behavior selection of artificial creatures. The knowledge links between input and behavior symbols are represented by the partial evaluation values of behaviors over each input symbol, and the degrees of consideration for input symbols are represented by the fuzzy measures. The proposed method selects a behavior through global evaluation by the fuzzy integral, as a multi-criteria decision making process, of knowledge link strengths with respect to the fuzzy measure values. The effectiveness of the proposed behavior selection method is demonstrated by experiments carried out with a synthetic character "Rity" in the 3D virtual environment. The results show that the artificial creatures with various characteristics can be successfully created by the proposed DoC-MoT. Moreover, training the created artificial creatures to modify their characteristics was more efficient in the DoC-MoT than the probability-based mechanism of thought (P-MoT), both in terms of the number of parameters to be set and the amount of time consumed.