Discrete Optimization for Agents

Many an agent needs to make a decision of what do to given a limited amount of resources. This is a discrete optimization problem. Recent advances in discrete optimization modelling technology have made it easier than ever before to model difficult ad hoc optimization problems, and apply off the shelf solving technology rapidly and efficiently, while rapid advances in discrete optimization solving technology have made it faster than ever before to solve such problems. In this talk I will illustrate how • modern languages allow us to capture the combinatorial substructure of discrete optimization problems thus allowing solving to be much more rapid; • how nogood learning can exponentially improve our ability to solve these problems; • and how these combine to allow us to rapidly resolve problems that are slowly changing, a case that seems particularly relevant to agents readjusting their plans in a changing environment My hope is that the agent community will be inspired to learn and use more of the tools and techniques developed by the discrete optimization community. CCS Concepts •Mathematics of computing → Discrete optimization; •Theory of computation→Constraint and logic programming; •Hardware → Theorem proving and SAT solving;