Date: Wednesday, 12.07.23 17:00 CET
Signup: If you would like to attend the talks please register here to get a Zoom link.
Abstract:
We make decisions in environments that change over time and under increasing degrees of uncertainty. The field of Dynamic Decision Making (DDM) studies how humans make decisions in such situations and how they learn from past decisions to adapt and improve their choices over time. The most well-known theory of DDM is Instance-Based Learning Theory (IBLT) (Gonzalez, Lerch & Lebiere, 2003).
IBLT emerged from a set of behavioral phenomena established in experiments that used Microworlds (complex interactive decision games) and from the efforts to implement computational algorithms that would replicate the human decision process involved. The theoretical process and mechanisms proposed in IBLT have been used in the computational implementation of a large number of models in multiple fields.
The power of the predictions of IBL models compared to human decisions has been demonstrated in a large diversity of tasks, including the control of carbon dioxide in the atmosphere, supply chain inventory management, search and rescue, navigation in non-stochastic situations, and dynamic resource allocation in cyber defense.
In this talk, I will present the theoretical principles of IBLT and illustrate how IBL models have been used to achieve a level of dynamic and adaptive autonomy in cyber defense. I will provide a high-level overview of the advances we have achieved by using a cognitive approach for modeling the attacker's and end-user's decisions, exploring various deception techniques, and performing empirical demonstrations of these deception techniques in tasks of increasing complexity and realism. Finally, I will conclude with a discussion of the future potential of IBL models in Human-Automation Teams more generally.
