A comparative review of decision-making approaches for realistic event-driven environments

Decision-making in complex, multi-process, event-driven environments, where information arrives as asynchronous event streams, poses distinct challenges compared to traditional scenarios. Factors such as concurrency, asynchronicity, partial observability, and complex interdependencies introduce significant modeling and computational difficulties and require thorough analysis and characterization. This paper offers a comparative review of existing decision-making approaches designed to address these challenges. Leveraging Endsley’s model of Situation Awareness, our analysis is structured around the cognitive processes of perception, comprehension, and projection of information. We examine various methodologies, including Temporal Planning and Modeling, Discrete Event Dynamic Systems, Event Processing, and Probabilistic Graphical Models, and identify their strengths, limitations, and applicability to complex, dynamic settings. Our findings underscore the necessity of a comprehensive framework that integrates the decision-action-perception loop by combining the perception of multimodal, semantically enriched data, the comprehension through online learning of stochastic event models emitted by multiple long-range processes, and the projection via decision-making that balances task performance with continuous model refinement. This highlights the value of hybrid solutions that combine the complementary strengths of different approaches to address the multifaceted challenges inherent across all levels of situational awareness and decision-making.