Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems
Published Online:4 Aug 2026https://doi.org/10.1287/ijds.2026.0182
References
- (2025) Toward futuristic autonomous experimentation—A surprise-reacting sequential experiment policy. IEEE Trans. Automation Sci. Engrg. 22:7912–7926.Google Scholar
- (2020) Anomaly detection at scale: The case for deep distributional time series models. Proc. Internat. Conf. Service-Oriented Comput. (Springer, Berlin, Heidelberg), 97–109.Google Scholar
- (2018) Clustering and unsupervised anomaly detection with l-2 normalized deep auto-encoder representations. Proc. Internat. Joint Conf. Neural Networks (IEEE, Piscataway, NJ), 1–6.Google Scholar
- (2002) A computational theory of surprise. Blaum M, Farrell PG, eds. Proc. Workshop Honoring Prof. Bob Mceliece on His 60th Birthday: Inform., Coding and Math. (Springer, Berlin, Heidelberg), 1–25.Google Scholar
- (2013) Novelty or surprise? Frontiers Psych. 4:907.Google Scholar
- (2022) From concept drift to model degradation: An overview on performance-aware drift detectors. Knowledge Based Systems 245:108632.Google Scholar
- (2009) Discriminative learning under covariate shift. J. Machine Learn. Res. 10(9):2137–2155.Google Scholar
- (2022) Anomaly detection in autonomous driving: A survey. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 4487–4498.Google Scholar
- (2010) Exploration vs. exploitation in active learning: A Bayesian approach. Proc. Internat. Joint Conf. Neural Networks (IEEE, Piscataway, NJ), 1–7.Google Scholar
- (2020) A mobile robotic chemist. Nature 583:237–241.Google Scholar
- (2020) Anomaly detection for autonomous guided vehicles using Bayesian surprise. Proc. IEEE/RSJ Internat. Conf. Intelligent Robots Systems (IEEE, Piscataway, NJ), 8148–8153.Google Scholar
- (2009) Active learning for object classification: From exploration to exploitation. Data Mining Knowledge Discovery 18:283–299.Google Scholar
- (2009) Generalization errors and learning curves for regression with multi-task Gaussian processes. Proc. 23rd Adv. Neural Inform. Processing Systems (ACM, New York), 279–287.Google Scholar
- (2020) Efficient closed-loop maximization of carbon nanotube growth rate using Bayesian optimization. Sci. Rep. 10:9040.Google Scholar
- (2015) Active hypothesis testing for anomaly detection. IEEE Trans. Inform. Theory 61(3):1432–1450.Google Scholar
- (1999) Elements of Information Theory (John Wiley & Sons, New York).Google Scholar
- (2024) Autonomous mobile robots for exploratory synthetic chemistry. Nature 635:890–897.Google Scholar
- (1984) Present position and potential developments: Some personal views statistical theory the prequential approach. J. Roy. Statist. Soc.: Ser. A (General) 147(2):278–290.Google Scholar
- (2023) Measuring surprise in the wild. Preprint, submitted May 12, https://arxiv.org/abs/2305.07733.Google Scholar
- (2013) Mutual information-based feature selection for multilabel classification. Neurocomputing 122:148–155.Google Scholar
- (2018) Balancing new against old information: The role of puzzlement surprise in learning. Neural Comput. 30(1):34–83.Google Scholar
- (2006) The permutation test for feature selection by mutual information. Proc. 14th Eur. Sympos. Artificial Neural Networks (ESANN, Bruges, Belgium), 239–244.Google Scholar
- (2015) Active inference and epistemic value. Cognitive Neurosci. 6(4):187–214.Google Scholar
- (2020) Mahalanobis distance based adversarial network for anomaly detection. Proc. IEEE Internat. Conf. Acoustics, Speech and Signal Processing (IEEE, Piscataway, NJ), 3192–3196.Google Scholar
- (2025) Dynamic exploration–exploitation trade-off in active learning regression with Bayesian hierarchical modeling. IISE Trans. 57(4):393–407.Google Scholar
- (2009) Bayesian surprise attracts human attention. Vision Res. 49(10):1295–1306.Google Scholar
- (2022) Autonomous experimentation systems and benefit of surprise-based Bayesian optimization. Proc. Internat. Sympos. Flexible Automation (ASME, Yokohama, Japan), 1–7.Google Scholar
- (2016) Space-filling designs for computer experiments: A review. Quality Engrg. 28(1):28–35.Google Scholar
- (2023) Null hypothesis test for anomaly detection. Phys. Lett. B 840:137836.Google Scholar
- (2015) A computational analysis of the neural bases of Bayesian inference. Neuroimage 106:222–237.Google Scholar
- (2020) Repad: Real-time proactive anomaly detection for time series. Proc. Internat. Conf. Advanced Inform. Networking Appl. (Springer), 1291–1302.Google Scholar
- (2023) Towards resilience in industry 5.0: A decentralized autonomous manufacturing paradigm. J. Manufacturing Systems 71:95–114.Google Scholar
- (2011) Towards fully autonomous driving: Systems and algorithms. Proc. IEEE Intelligent Vehicles Sympos (IEEE, Piscataway, NJ), 163–168.Google Scholar
- (2021) Learning in volatile environments with the Bayes factor surprise. Neural Comput. 33(2):269–340.Google Scholar
- (2022) Anomaly detection and correction of optimizing autonomous systems with inverse reinforcement learning. IEEE Trans. Cybernetics 53(7):4555–4566.Google Scholar
- (2020) Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6(20):eaaz8867.Google Scholar
- (2023) Scaling deep learning for materials discovery. Nature 624:80–85.Google Scholar
- (2022) A taxonomy of surprise definitions. J. Math. Psych. 110:102712.Google Scholar
- (2016) Artificial immune system via Euclidean distance minimization for anomaly detection in bearings. Mechanical Systems Signal Processing 76:380–393.Google Scholar
- (2012) A unifying view on dataset shift in classification. Pattern Recognition 45(1):521–530.Google Scholar
- (2019) Anomaly detection with multiple-hypotheses predictions. Proc. 36th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 97 (PMLR, New York), 4800–4809.Google Scholar
- (2016) Autonomy in materials research: A case study in carbon nanotube growth. NPJ Comput. Material 2:16031.Google Scholar
- (2003) Estimation of entropy and mutual information. Neural Comput. 15(6):1191–1253.Google Scholar
- (2012) An autonomous manufacturing system based on swarm of cognitive agents. J. Manufacturing Systems 31(3):337–348.Google Scholar
- (2021) Human inference in changing environments with temporal structure. Psych. Rev. 128(5):879–912.Google Scholar
- (2024) An augmented surprise-guided sequential learning framework for predicting the melt pool geometry. J. Manufacturing Systems 75:56–77.Google Scholar
- (2021) High-tech defense industries: Developing autonomous intelligent systems. Appl. Sci. 11(11):4920.Google Scholar
- (1993) Alternatives to the median absolute deviation. J. Amer. Statist. Assoc. 88(424):1273–1283.Google Scholar
- (2019) F-anoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Anal. 54:30–44.Google Scholar
- (1948) A mathematical theory of communication. Bell System Tech. J. 27(3):379–423.Google Scholar
- (2007) Covariate shift adaptation by importance weighted cross validation. J. Machine Learn. Res. 8(5):985–1005.Google Scholar
- (2023) An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624:86–91.Google Scholar
- (1994) Generalizing the Fano inequality. IEEE Trans. Inform. Theory 40(4):1247–1251.Google Scholar
- (2013) Online anomaly detection for hard disk drives based on Mahalanobis distance. IEEE Trans. Reliability 62(1):136–145.Google Scholar
- (2014) A survey of distance and similarity measures used within network intrusion anomaly detection. IEEE Comm. Surveys Tutorials 17(1):70–91.Google Scholar
- (2020) A survey of autonomous driving: Common practices and emerging technologies. IEEE Access 8:58443–58469.Google Scholar
- (2022) A Bayesian surprise approach in designing cognitive radar for autonomous driving. Entropy 24(5):672.Google Scholar
- (2023) Concept drift monitoring and diagnostics of supervised learning models via score vectors. Technometrics 65(2):137–149.Google Scholar
- (2016) Continuous anomaly detection in satellite image time series based on z-scores of season-trend model residuals. Proc. IEEE Internat. Geosci. Remote Sensing Sympos. (IEEE, Piscataway, NJ), 3410–3413.Google Scholar
- (2016) An overview of concept drift applications. Big Data Analysis: New Algorithms New Society 16: 91–114.Google Scholar

