Search results for "Action Selection"
showing 6 items of 6 documents
What Will You Do Next? A Cognitive Model for Understanding Others’ Intentions Based on Shared Representations
2013
Goal-directed action selection is the problem of what to do next in order to progress towards goal achievement. This problem is computationally more complex in case of joint action settings where two or more agents coordinate their actions in space and time to bring about a common goal: actions performed by one agent influence the action possibilities of the other agents, and ultimately the goal achievement. While humans apparently effortlessly engage in complex joint actions, a number of questions remain to be solved to achieve similar performances in artificial agents: How agents represent and understand actions being performed by others? How this understanding influences the choice of ag…
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
2018
Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g.\ different environments or agent morphologies. In the literature, paradigms that share this independence have been summarised under the notion of in…
Generic Inhibition of the Selected Movement and Constrained Inhibition of Nonselected Movements during Response Preparation
2014
Abstract Previous studies have identified two inhibitory mechanisms that operate during action selection and preparation. One mechanism, competition resolution, is manifest in the inhibition of the nonselected response and attributed to competition between candidate actions. The second mechanism, impulse control, is manifest in the inhibition of the selected response and is presumably invoked to prevent premature response. To identify constraints on the operation of these two inhibitory mechanisms, we manipulated the effectors used for the response alternatives, measuring changes in corticospinal excitability with motor-evoked potentials to TMS. Inhibition of the selected response (impulse …
Simulation and anticipation as tools for coordinating with the future
2013
A key goal in designing an artificial intelligence capable of performing complex tasks is a mechanism that allows it to efficiently choose appropriate and relevant actions in a variety of situations and contexts. Nowhere is this more obvious than in the case of building a general intelligence, where the contextual choice and application of actions must be done in the presence of large numbers of alternatives, both subtly and obviously distinct from each other. We present a framework for action selection based on the concurrent activity of multiple forward and inverse models. A key characteristic of the proposed system is the use of simulation to choose an action: the system continuously sim…
Learning Automata Based Q-learning for Content Placement in Cooperative Caching
2019
An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is p…
A Comparative Analysis of Multiple Biasing Techniques for $Q_{biased}$ Softmax Regression Algorithm
2021
Over the past many years the popularity of robotic workers has seen a tremendous surge. Several tasks which were previously considered insurmountable are able to be performed by robots efficiently, with much ease. This is mainly due to the advances made in the field of control systems and artificial intelligence in recent years. Lately, we have seen Reinforcement Learning (RL) capture the spotlight, in the field of robotics. Instead of explicitly specifying the solution of a particular task, RL enables the robot (agent) to explore its environment and through trial and error choose the appropriate response. In this paper, a comparative analysis of biasing techniques for the Q-biased softmax …