Multiagent negotiation for fair and unbiased resource allocation

This paper proposes a novel solution for the n agent cake cutting (resource allocation) problem. We propose a negotiation protocol for dividing a resource among n agents and then provide an algorithm for allotting portions of the resource. We prove that this protocol can enable distribution of the resource among n agents in a fair manner. The protocol enables agents to choose portions based on their internal utility function, which they do not have to reveal. In addition to being fair, the protocol has desirable features such as being unbiased and verifiable while allocating resources. In the case where the resource is two-dimensional (a circular cake) and uniform, it is shown that each agent can get close to l/n of the whole resource.Utility theory ; Utility function ; Bargaining ; Artificial intelligence ; Resource allocation ; Multiagent system

Multiagent Negotiation for Fair and Unbiased Resource Allocation

This paper proposes a novel solution for the n agent cake cutting (resource allocation) problem. We propose a negotiation protocol for dividing a resource among n agents and then provide an algorithm for allotting portions of the resource. We prove that this protocol can enable distribution of the resource among n agents in a fair manner. The protocol enables agents to choose portions based on their internal utility function, which they do not have to reveal. In addition to being fair, the protocol has desirable features such as being unbiased and verifiable while allocating resources. In the case where the resource is two-dimensional (a circular cake) and uniform, it is shown that each agent can get close to l/n of the whole resource

Multiagent negotiation for fair and unbiased resource allocation

This paper proposes a novel solution for the n agent cake cutting (resource allocation) problem. We propose a negotiation protocol for dividing a resource among n agents and then provide an algorithm for allotting portions of the resource. We prove that this protocol can enable distribution of the resource among n agents in a fair manner. The protocol enables agents to choose portions based on their internal utility function, which they do not have to reveal. In addition to being fair, the protocol has desirable features such as being unbiased and verifiable while allocating resources. In the case where the resource is two-dimensional (a circular cake) and uniform, it is shown that each agent can get close to l/n of the whole resource

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section

Enabling cooperative and negotiated energy exchange in remote communities

Energy poverty at the household level is defined as the lack of access to electricity and reliance on the traditional use of biomass for cooking, and is a serious hindrance to economic and social development. It is estimated that 1.3 billion people live without access to electricity and almost 2.7 billion people rely on biomass for cooking, a majority of whom live in small communities scattered over vast areas of land (mostly in the Sub-Saharan Africa and the developing Asia). Access to electricity is a serious issue as a number of socio-economic factors, from health to education, rely heavily on electricity. Recent initiatives have sought to provide these remote communities with off-grid renewable microgeneration infrastructure such as solar panels, and electric batteries. At present, these resources (i.e., microgeneration and storage) are operated in isolation for individual home needs, which results in an inefficient and costly use of resources, especially in the case of electric batteries which are expensive and have a limited number of charging cycles. We envision that by connecting homes together in a remote community and enabling energy exchange between them, this microgeneration infrastructure can be used more efficiently. Against this background, in this thesis we investigate the methods and processes through which homes in a remote community can exchange energy. We note that remote communities lack general infrastructure such as power supply systems (e.g., the electricity grid) or communication networks (e.g., the internet), that is taken for granted in urban areas. Taking these challenges into account and using insights from knowledge domains such game theory and multi-agent systems, we present two solutions: (i) a cooperative energy exchange solution and (ii) a negotiated energy exchange solution, in order to enable energy exchange in remote communities.Our cooperative energy exchange solution enables connected homes in a remote community to form a coalition and exchange energy. We show that such coalition a results in two surpluses: (i) reduction in the overall battery usage and (ii) reduction in the energy storage losses. Each agents's contribution to the coalition is calculated by its Shapley value or, by its approximated Shapley value in case of large communities. Using real world data, we empirically evaluate our solution to show that energy exchange: (i) can reduce the need for battery charging (by close to 65%) in a community; compared with when they do not exchange energy, and (ii) can improve the efficient use of energy (by up to 10% under certain conditions) compared with no energy exchange. Our negotiated energy exchange solution enables agents to negotiate directly with each other and reach energy exchange agreements. Negotiation over energy exchange is an interdependent multi-issue type of negotiation that is regarded as very difficult and complex. We present a negotiation protocol, named Energy Exchange Protocol (EEP), which simplifies this negotiation by restricting the offers that agents can make to each other. These restrictions are engineered such that agents, negotiation under the EEP, have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes (within restricted the set of offers) in a decentralised manner. Using real world data, we empirically evaluate our protocol and show that, in this instance, a society of agents can: (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%, compared to when they do not exchange energy

A Comparative Study of Argumentation-and Proposal-Based Negotiation

Summary. Recently, argumentation-based negotiation has been proposed as an alternative to classical mechanism design. The main advantage of argumentation-based negotiation is that it allows agents to exchange complex justification positions rather than just simple proposals. Its proponents maintain that this property of argumentation protocols can lead to faster and beneficial agreements when used for complex multiagent negotiation. In this paper, we present an empirical comparison of argumentation-based negotiation to proposal-based negotiation in a strategic two-player scenario, using a game-theoretic solution as a benchmark, which requires full knowledge of the stage games. Our experiments show that in fact the argumentation-based approach outperforms the proposal-based approach with respect to the quality of the agreements found and the overall time to agreement

Literature Overview on the Field of Co-opetition

Co-opetition is a perspective on business relationships which highlights the ambivalence of competition and cooperation. Game theory is regarded as the mathematical tool for solving co-opetition related problems. The major step for introducing "co-opetition" into public discussion and economic research has been made by Brandenburger and Nalebuff in 1996. However they target a non-professional readership. A multitude of publications has followed, where the authors mostly focus on specific aspects of the problem and investigate particular industries. This paper gives a comprehensive literature overview on the field of co-opetition

TOKEN-BASED APPROACH FOR SCALABLE TEAMCOORDINATION

To form a cooperative multiagent team, autonomous agents are required to harmonize activities and make the best use of exclusive resources to achieve their common goal. In addition, to handle uncertainty and quickly respond to external environmental events, they should share knowledge and sensor in formation. Unlike small team coordination, agents in scalable team must limit the amount of their communications while maximizing team performance. Communication decisions are critical to scalable-team coordination because agents should target their communications, but these decisions cannot be supported by a precise model or by complete team knowledge.The hypothesis of my thesis is: local routing of tokens encapsulating discrete elements of control, based only on decentralized local probability decision models, will lead to efficient scalable coordination with several hundreds of agents. In my research, coordination controls including all domain knowledge, tasks and exclusive resources are encapsulated into tokens. By passing tokens around, agents transfer team controls encapsulated in the tokens. The team benefits when a token is passed to an agent who can make use of it, but communications incur costs. Hence, no single agent has sole responsible over any shared decision. The key problem lies in how agents make the correct decisions to target communications and pass tokens so that they will potentially benefit the team most when considering communication costs.My research on token-based coordination algorithm starts from the investigation of random walk of token movement. I found a little increase of the probabilities that agents make the right decision to pass a token, the overall efficiency of the token movement could be greatly enhanced. Moreover, if token movements are modeled as a Markov chain, I found that the efficiency of passing tokens could be significantly varied based on different network topologies.My token-based algorithm starts at the investigation of each single decision theoretic agents. Although under the uncertainties that exist in large multiagent teams, agents cannot act optimal, it is still feasible to build a probability model for each agents to rationally pass tokens. Specifically, this decision only allow agent to pass tokens over an associate network where only a few of team members are considered as token receiver.My proposed algorithm will build each agent's individual decision model based on all of its previously received tokens. This model will not require the complete knowledge of the team. The key idea is that I will make use of the domain relationships between pairs of coordination controls. Previously received tokens will help the receiver to infer whether the sender could benefit the team if a related token is received. Therefore, each token is used to improve the routing of other tokens, leading to a dramatic performance improvement when more tokens are added. By exploring the relationships between different types of coordination controls, an integrated coordination algorithm will be built, and an improvement of one aspect of coordination will enhance the performance of the others