Sentence examples for behaviour of agents from inspiring English sources

In particular, the concept of norm is defined at design time to assure the correct behaviour of agents in such systems.

It supports the idea that PT based agents may be a better pick to model the behaviour of agents in risky environments.

In this case, the behaviour of agents with each other starts with indifference and leads to a rather null interaction.

The literature on large episodes of fiscal consolidation focuses on the possible expansionary effects linked to the forward-looking behaviour of agents (e.g., Giavazzi and Pagano, 1990; Alesina et al., 2002) and on the effectiveness of these episodes in durably improving the state of public finances (e.g., Alesina and Ardagna, 1998).

This allows this emerging paradigm to overcome the objection challenging the paradigm of fault: uncertainty surrounding science and technology and its impacts make it impossible to establish the causal chains back to the (faulty) behaviour of agents that are necessary to determine liability.

Their role in process evaluation would be based on their capacity to explain the motivation and behaviour of agents in the course of an intervention complex or otherwise.

Comparison of the impact of behaviours of agents is then facilitated by simply swapping a behaviour with another one.

∎ Behaviours of agents can be added/changed without coding – both at the beginning when setting up the model, and during the run, ∎ A user can try different models without going into the code, but simply by combining different aspects of the model.

In artificial intelligence, and in particular in the multi-agent systems research field, social laws, norms, and sanctions have been widely proposed as flexible means for coordinating the behaviour of autonomous agents in multi-agent settings.

Differing local conditions lead to different behavioural trajectories of the individual agents, and the heterogeneous behaviour of individual agents leads to the aggregated system dynamics.

This is then compared with a number of agent-based scenarios, with varying restrictions on fishing inputs, where each individual agent seeks to maximise its own fishing returns subject to the behaviour of other agents.