Autonomous adaptation of software agents in the support of human activities

To better illustrate it, let us consider a scenario in which we want to optimize the power consumption of a smart building, composed of several spaces in which devices (e.g.

In the final phase, the agent uses the feedback from the human counterpart to adapt its causal knowledge as well as its reasoning behind the behavior adaptation.. Finally,

In this paper the embedding results in the questions of strong approximation on Fourier series are considered.. Previous related re- sults from Leindler’s book [2] and the paper [5]

It is also a task of the DerivePEM step to endow beliefs (derived from percepts) of reactive emotions associated to the agent emotional and cognitive state (the function of the

The most important barrier to improved policy is often not a lack of research data, but poor knowledge management: A failure to ensure that research is relevant to the requirements

The objective is the following: given a simple environment-driven evolution- ary adaptation algorithm distributed over a swarm of agents, can we observe the emergence of

In any case, to keep things simple, we consider two layers: the Decisional Layer and the Functional Layer (see Fig. In fact it boils down to two types of software components: i)

Our structure called Autonomous Lighting Agents starts by using an agent-based scene discovery algorithm and is later used to make decisions during rendering inducing less photons