Now or Never: Negotiating Efficiently with Unknown or Untrusted Counterparts*

Abstract

We define a new protocol rule, Now or Never (NoN), for bilateral negotiation processes which allows self-motivated competitive agents to efficiently carry out multi-variable negotiations with remote untrusted parties, where privacy is a major concern and agents know nothing about their opponent. By building on the geometric concepts of convexity and convex hull, NoN ensures a continuous progress of the negotiation, thus neutralising malicious or inefficient opponents. In particular, NoN allows an agent to derive in a finite number of steps, and independently of the behaviour of the opponent, that there is no hope to find an agreement. To be able to make such an inference, the interested agent may rely on herself only, still keeping the highest freedom in the choice of her strategy. We also propose an actual NoN-compliant strategy for an automated agent and evaluate the computational feasibility of the overall approach on both random negotiation scenarios and case studies of practical size.

Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV Localization in Critical Areas via Computational Geometry

“…this is a specialized version of the projection defined in [40]). Note that, given point X and region R, the set of points Y such that the segment XY has a distance from R at most a given threshold β can be defined as proj(X, bloat(R, β)).…”

Arguing and negotiating using incomplete negotiators profiles

“…More specifically, the main difference with works such as [3,5,31,22,40,27,35,34] is that in these works the negotiation theories of the agents do not contain any kind of information on the opponents profiles. Therefore the bidding strategies (by using different policies) are based on the proponents agents own theories incrementally enhanced with the arguments sent by the opponents during the negotiation.…”

MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks*

“…All the above steps extensively use standard AI reasoners to produce correct-by-construction outputs. In particular, the first two steps define multiple helper computational geometry problems similar to those addressed in [13], and solve them using a standard MILP solver. As for the third step (where we define our main optimisation problem), we experiment with AI reasoners based on different paradigms, namely MILP, PB-SAT, and SMT/OMT.…”