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

There are many examples where large amounts 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. Also, there are several activities that an agent might plan and perform on the web where one or more stages imply the payment of fees. For instance, consider the issue of buying resources in a cloud computing context where the objective of the agent is to obtain the best possible configuration of a certain application within given budget constraints. In this paper we consider the software-engineering problem of how to practically empower agents with the capability to perform budget-constrained 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's actions, also according to policies/preferences. We further enhance the ACE framework by making the agent-components interaction mechanism parametric with respect to the actual modules that an agent may dynamically decide to exploit. We discuss the proposed extensions on a realistic case-study.