Action selection

Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behavior. One problem for understanding action selection is determining the level of abstraction used for specifying an "act". At the most basic level of abstraction, an atomic act could be anything from contracting a muscle cell to provoking a war. Typically for any one action-selection mechanism, the set of possible actions is predefined and fixed. Most researchers working in this field place high demands on their agents: The acting agent typically must select its action in dynamic and unpredictable environments. The agents typically act in real time; therefore they must make decisions in a timely fashion. The agents are normally created to perform several different tasks. These tasks may conflict for resource allocation (e.g. can the agent put out a fire and deliver a cup of coffee at the same time?) The environment the agents operate in may include humans, who may make things more difficult for the agent (either intentionally or by attempting to assist.) The agents themselves are often intended to model animals or humans, and animal/human behaviour is quite complicated. For these reasons action selection is not trivial and attracts a good deal of research. The main problem for action selection is complexity. Since all computation takes both time and space (in memory), agents cannot possibly consider every option available to them at every instant in time. Consequently, they must be biased, and constrain their search in some way.

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Nikolaos Geroliminis, Can Chen

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

Informs2024

Vision Transformer Adapters for Generalizable Multitask Learning

Sabine Süsstrunk, Mathieu Salzmann, Deblina Bhattacharjee

We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains. Integrated into an off-the-shelf vision transformer backbone, our adapters can simultaneously solve mu ...

2023

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Investigating neural resource allocation in the sensorimotor control of extra limbs

Vision Transformer Adapters for Generalizable Multitask Learning

Investigating neural resource allocation in the sensorimotor control of extra limbs

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Vision Transformer Adapters for Generalizable Multitask Learning