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Goal selection and action selection are important topics in agent research. This paper first describes an agent model, Goal Net, which supports multiple goal selection methods and action selection mechanisms for an agent. Then a goal selection algorithm and action selection mechanisms supported by Goal Net are presented. Examples from real system development are also given to illustrate the algorithm and mechanisms.

Cognitive map (CM), fuzzy cognitive map (FCM) are two related tools for modeling human being's cognition and facilitating machine deduction over the cognitive model accordingly. FCM has extended CM by modeling the strength of the causal relationship. To acquire the ability of modeling the strength of cause and effect, multi-value FCM (MVFCM) is formally presented in this paper, which uses mutli-value concepts. Not surprisingly, FCM is more complex than CM and MVFCM is more complex than FCM. This paper proves that there exists a theoretical equivalence among MVFCM, FCM and CM. It shows that for every MVFCM, there exists a FCM, or a CM that represents the MVFCM. This result allows domain experts to model applications with more descriptive MVFCM form and perform theoretical analysis with the simpler CM form. The equivalence among the models also provides strong support to the theoretical analysis of the models and flexibility to their applications.

In this paper, we present the probabilistic fuzzy cognitive map (PFCM) which is a novel extension of FCM theory. Each concept in PFCM is extended to a fuzzy event that models not only the fuzzy degree but also the fuzzy probability of both the cause and the effect concepts. PFCM enhances the capability of conventional FCMs to handle both randomness and fuzziness which are necessary to model the uncertainty involved in inference process of complex causal system. A formalized inference process of PFCMs is presented for adjustments on probability of fuzzy events and for dynamic update of causal weights. This enables PFCM to synthetically analyze the impacts of randomness and fuzziness on causal inference process. The simulation result shows a good match to the above features of PFCM. PFCM, as an initial attempt, provides a heuristic approach to model the uncertainty of complex causal systems and opens a collection of interesting research issues for further research.