L. Rutkowski et al. (Eds.): ICAISC 2012, Part II, LNCS 7268, pp. 706–715, 2012. © Springer-Verlag Berlin Heidelberg 2012 Hybrid Anticipatory Networks Andrzej M.J. Skulimowski AGH University of Science and Technology, Chair of Automatic Control, Decision Sciences Laboratory, al. Mickiewicza 30, 30-050 Kraków, Poland ams@agh.edu.pl Abstract. This paper presents a theory of hybrid anticipatory networks that ge- neralizes earlier models of consequence anticipation in multicriteria decision problems. We assume that the decision maker takes into account the anticipated outcomes of future decision problems linked by the causal relations with the present decision problem. This can be represented by a multigraph, where deci- sion problems are modeled as nodes linked causally and by one or more addi- tional anticipation relations. These types of multigraphs are termed anticipatory networks. Hybrid anticipatory systems may contain additional models of ran- dom and non-cooperative game decisions. Constructive solution methods for decision problems modeled by anticipatory networks are discussed as well. Further, we present a generalization of hybrid anticipatory networks, known as superanticipatory systems. In the final section we discuss some of their applica- tions in the design of decision-making rules in autonomous robotic systems and in filtering technology development scenarios. Keywords: Anticipatory networks, decision theory, multicriteria optimization, analysis of consequences, superanticipatory systems. 1 Introduction The introduction of anticipatory networks as models of future consequences in a deci- sion-making process was inspired by the idea formulated in [6] as “To use anticipated future consequences of a decision as a source of additional preference information in multicriteria decision problems”. The exploration of such anticipatory feedback is possible owing to the following assumptions: 1. A decision maker is responsible for solving a decision problem which corresponds to the starting node of the anticipatory network. 2. There exist estimates (forecasts or foresight scenarios) of future decision problem formulations, their solution rules, decision makers’ preferences and of the relations binding their anticipated outcomes with the current problem. 3. The decision maker knows the causal structure of future decision problems that are modeled by the other nodes of the network, in particular the way in which problem parameters are influenced by solutions to preceding decision problems. The first and third assumptions allow us to model the impact of a decision to-be-made on any subsequent problem in the network. The second assumption is a basis for