Automated negotiation with Gaussian process-based utility models

Designing agents that can efficiently learn and integrate user's preferences into decision making processes is a key challenge in automated negotiation. While accurate knowledge of user preferences is highly desirable, eliciting the necessary information might be rather costly, since frequent user interactions may cause inconvenience. Therefore, efficient elicitation strategies (minimizing elicitation costs) for inferring relevant information are critical. We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate it into a (belief) Markov Decision Process paradigm which formalizes automated negotiation processes with incomplete information. Our utility model, which naturally maps ordinal preferences (inferred from the user) into (random) utility values (with the randomness reflecting the underlying uncertainty), provides the basic quantitative modeling ingredient for automated (agent-based) negotiation

Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives

This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences. The heterogeneity arises from prosumers' evaluation of energy contracts through multiple societal and environmental criteria and the prosumers' private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans consisting of the energy volume to be exchanged at that period and the return time of the exchanged energy. The negotiating prosumers navigate through a common negotiation domain consisting of potential energy contracts and evaluate those contracts from their valuations on the entailed criteria against a utility function that is robust against generation and demand uncertainty. From the repeated interactions, a prosumer gradually learns about the compatibility of its peers in reaching energy contracts that are closer to Nash solutions. Empirical evaluation on real demand, generation and storage profiles – in multiple system scales – illustrates that the proposed negotiation based strategy can increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy representing the status quo strategy. We thus elicit system benefits from peer-to-peer flexibility exchange already without any central coordination and market operator, providing a simple yet flexible and effective paradigm that complements existing markets

Robust online planning with imperfect models

Environment models are not always known a priori, and approximating stochastic transition dynamics may introduce errors, especially if only a small amount of data is available and/or model misspecification is

Preference Learning in Automated Negotiation Using Gaussian Uncertainty Models

In this paper, we propose a general two-objective Markov Decision Process (MDP) modeling paradigm for automated negotiation with incomplete information, in which preference elicitation alternates with negotiation actions, with the objective to optimize negotiation outcomes. The key ingredient in our MDP framework is a stochastic utility model governed by a Gaussian law, formalizing the agent's belief (uncertainty) over the user's preferences. Our belief model is fairly general and can be updated in real time as new data becomes available, which makes it a fundamental modeling tool

SLA-mechanisms for electricity trading under volatile supply and varying criticality of demand (Extended Abstract)

The increasing adoption of renewable power generation makes volatile quantities of electricity available, the delivery of which cannot be guaranteed, if sold. However, if not sold, the electricity might need to be curtailed, thus foregoing potential profits. In this paper we adapt service level agreements (SLAs) for the future smart electricity grid, where generation will primarily depend on volatile and istributed renewable power sources, and where buyers' ability to cope with uncertainty may vary significantly. We propose a contracting framework through SLAs to allocate uncertain power generation to buyers of varying preferences. These SLAs comprise quantity, reliability and price. We define a characterization of the value degradation of tolerant and critical buyers with regards to the uncertainty of electricity delivery (generalizing the Value of Lost Load, VoLL). We consider two mechanisms (sequential second-price auction and VCG) that allocate SLAs based on buyer bids. We further study the incentive compatibility of the proposed mechanisms, and show that both mechanisms ensure that no buyer has an incentive to misreport its valuation. We experimentally compare their performance and demonstrate that VCG dominates alternative allocations, while vastly improves the efficiency of the proposed system when compared to a baseline allocation considering only the VoLL. This article lays the ground work for distributed energy trading under uncertainty, thereby contributing an essential component to the future smart grid

A multi-scale energy demand model suggests sharing market risks with intelligent energy cooperatives

In this paper, we propose a multi-scale model of energy demand that is consistent with observations at a macro scale, in our use-case standard load profiles for (residential) electric loads. We employ the model to study incentives to assume the risk of volatile market prices for intelligent energy cooperatives at different aggregation scales of energy consumption. Next to scale, we investigate the benefits of demand response, more precisely intelligent scheduling of time-shiftable electric processes, and virtual storage intraday and between days. Results show that the increasing electrification and introduction of flexibilities (electric vehicles, thermal applications, storage, etc.) is going to make market participation viable for smaller groups of consumers. Retailers may thus introduce innovative tariffs for intelligent energy cooperatives to share the risk of volatility in wholesale markets for electricity