Augmenting agent computational environments with quantitative reasoning modules and customizable bridge rules

There are many examples where large amount of data might be potentially accessible to an agent, but the agent is constrained by the available budget since access to knowledge bases is subject to fees. There are also several activities that an agent might perform on the web where one or more stages imply the payment of fees: for instance, buying resources in a cloud computing context where the objective of the agent is to obtain the best possible configuration of a certain application withing given budget constraints. In this paper we consider the softwareengineering problem of how to practically empower agents with the capability to perform such kind of reasoning in a uniform and principled way. To this aim, we enhance the ACE component-based agent architecture by means of a device for practical and computationally affordable quantitative reasoning, whose results actually determine one or more courses of agent\u2019s actions, also according to policies/preferences

Augmenting agent computational environments with quantitative reasoning modules and customizable bridge rules

There are many examples where large amount of data might be potentially accessible to an agent, but the agent is constrained by the available budget since access to knowledge bases is subject to fees. There are also several activities that an agent might perform on the web where one or more stages imply the payment of fees: for instance, buying resources in a cloud computing context where the objective of the agent is to obtain the best possible configuration of a certain application withing given budget constraints. In this paper we consider the softwareengineering problem of how to practically empower agents with the capability to perform such kind of reasoning in a uniform and principled way. To this aim, we enhance the ACE component-based agent architecture by means of a device for practical and computationally affordable quantitative reasoning, whose results actually determine one or more courses of agent\u2019s actions, also according to policies/preferences