Asserting that current approaches to agent-oriented systems intended for open, heterogeneous, dynamic and uncertain environments are either unaffordable (with scarce resources) or ineffective (as regards user expectations), the paper aims at proposing a generic software framework based on emergence as main paradigm (within a blend with conventional ones, to attain a lever effect) and on non-algorithmic approaches to treat uncertainty at lower echelons (to suit complex application requirements even in very dynamic environments). This target is split into four specific objectives: a) investigating the relationships between complexity and emergence from the standpoint of modern artificial intelligence; b) showing that structural complexity can be dealt with through simulated emergence (e.g., via stigmergic coordination) and cognitive complexity through emulated emergence (e.g., via self-aware agents); c) investigating the (in)adequacy of logics and prediction methods used to handle uncertainty due to future contingents; d) outlining the path for developing affordable non-algorithmic mechanisms to deal with effectively. Since the emphasis is here on computer science aspects, design and implementation details about the mechanisms will be given in future papers. At this research stage, the results seem very promising and reveal significant potential for transdisciplinary projects (the main fields involved: complexity theory, synergetics, trivalent logics, cognetics).
Emergence; Uncertainty; Open, heterogeneous, dynamic and uncertain environments (OHDUE); Agent-oriented software engineering (AOSE); Stigmergy; Agent self-awareness.
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