Our group has experience and ongoing European projects with industry in the area of planning and (multi-agent) learning for negotiations, markets, and smart grids. We host both visitors for collaborative applied research, as well as pure research oriented projects in areas such as multi-agent systems, negotiation, game theory, game AI, planning, search, and machine learning. Successful applicants will be working under supervision of IAS researchers: Tim Baarslag, Hendrik Baier, and Valentin Robu. The applicant needs to be currently enrolled as a Master's or PhD student. If the candidate is registered at a European university with a performance rate of 8 or higher as evidenced by a list of results provided by the university the candidate can be eligible for an allowance. For non-EU citizens special regulations may apply.
Possible ideas are:
Negotiation and Preference modelling
Negotiation algorithms and elicitation
Computers that are able to negotiate are already common practice in areas such as high frequency trading, and are now finding applications in domains closer to home, such as the smart grid, real estate, the Internet of Things, and autonomous vehicles. These negotiations involve not just financial optimizations but balanced trade-offs between multiple issues, such as cost and convenience. Such agents can autonomously negotiate and coordinate with others in our stead, to reach outcomes and agreements in our interest.
In such situations, the agent represents a user with individual and a priori unknown preferences, which are costly to elicit due to the user bother this incurs. Therefore, the agent needs to strike a balance between increasing the user model accuracy and the inconvenience caused by interacting with the user. To do so, we require a tractable metric for the value of information in an ensuing negotiation, which until now has not been available.
Typical research questions include:
- How can we design a negotiation decision model that can capture and reason about uncertain utility information about the user (as well as the opponent) and can query the user for more preference information?
- What are effective bidding strategies?
- Can we formulate a tractable measure of the estimated outcome of a negotiation?
- What is a good stopping criterion that can weigh the estimated post-query value of information against the elicitation costs?
A (partial) solution to these questions would yield entirely new systems (procurement platforms being one example) that can negotiate on behalf of a user and that have the ability to query a user to specify their preferences when needed. This fits in a broader vision of an interactive negotiation agent that has knowledge about what questions can be asked at what costs, and can decide, using our method, which question is worth posing given the utility expected to arise from it.
These topics are tightly connected to the research in our group, which includes topics such as cooperation & competition, automated negotiation, and learning. An eligible candidate has good mathematical skills, and a passion for iterative research and design (i.e. come up with of a solution, try it out theoretically or in simulation, and re-assess in an iterative fashion).
Interested applicants may contact Tim Baarslag for more information.
Negotiation in Energy Cooperatives
An energy cooperative is a group of people who share their energy resources, such as their batteries and renewables (e.g. solar panels). By working together, energy cooperatives can reduce their overall costs: for example, when someone produces excess energy and their battery is fully charged, they can share it with their neighbor instead of selling it back to the grid. Of course, this gesture should be returned at some point, and for this, members of an energy cooperative can use negotiation to make deals about the amount of energy to share with each other, and the best time to do so. The candidate is invited to come up with a negotiation protocol for a simple, hands-on scenario (e.g. day/night transfer) and implement a simulation of such energy exchanges. The negotiation platform Genius can be used as a platform to build negotiation agents that represent the members of the cooperative and study their interactions.
Typical research questions include:
- Every cooperative member can have different characteristics with respect to how 'tough' they are in the negotiation. How does the competitiveness of the members influence the system aggregate (e.g. using measures such as the price of anarchy)?
- What are essential characteristics of its members for a cooperative to remain viable?
Members of the cooperative interact on a day-to-day basis, so it is important for them to build a good relationship with their peers. How should the members balance their short-term preferences with their follow-up interactions?
These topics are tightly connected to the research in our group, which includes topics such as cooperation & competition, automated negotiation, and learning. The successful applicant can work on building autonomous agents with impact on the real world. An eligible candidate has good programming and writing skills, and a passion for iterative research and design (i.e. come up with of a solution, try it out theoretically or in simulation, and re-assess in an iterative fashion).
Interested applicants in these topics may contact Tim Baarslag for more information.
Explainable and collaborative AI agents in games and real-world domains
As we work with AI and rely on AI for more and more decision-making processes that influence our daily lives, issues around user understanding of such processes have increased in urgency. Aimed at goals such as supporting trust in AI, enhancing collaboration with AI, and enabling transparency of AI decision-making, the research area of explainable AI (XAI) has rapidly developed. In this context, our group is working on explainable sequential decision-making systems.
By explainable we mean human-centric AI agents that are understood by their users, that understand their users, and that are effective and trustworthy collaborators for their users. By sequential decision making we mean tasks that extend over time, and require an autonomous agent to make many smart choices to achieve some user goal.
To achieve this, our research focuses on planning, required for acting towards long-term goals; on learning, required for acting in unknown environments; and on the explainability of planning and learning, required for successful human-AI collaboration. We need systems that learn, plan, and are able to communicate what they've learned and discuss what they're planning. Application areas are both adversarial or collaborative scenarios with several agents, as well as optimization problems with a single agent.
In order to tackle these challenges, we often use a variety of traditional and digital games as testbeds. Games can be used to cleanly model and precisely study many challenging decision-making problems. We then transfer our ideas to real-world problems in collaborative projects with industry, in sectors such as logistics and transportation, smart manufacturing, and sustainable energy.
A typical internship project within this area could be centred around a research topic such as:
- Integrating machine learning (including generative AI), optimization and planning for decision-making in complex domains;
- Planning and learning on multiple levels of abstraction and multiple timescales (strategy vs. tactics, long-term vs. short-term goals), and communicating plans accordingly;
- Modelling and learning user needs and preferences, and collaboratively working with users on solutions to their problems;
- Discussing with users which future courses of action are available, which outcomes are preferable, what the trade-offs are between them, and how to best achieve them.
These topics are tightly connected to the research in our group, which includes topics such as cooperation & competition, automated negotiation, and learning. A suitable candidate has good programming and writing skills and a passion for iterative research and design (i.e. coming up with a solution, trying it out theoretically or in simulation, and re-assessing in an iterative fashion).
Interested applicants can contact Hendrik Baier for more information.
