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Many practical scenarios involve solving a social choice problem: a group of self-interested agents have to agree on an outcome that best fits their combined preferences. We assume that each outcome presents a certain utility to an agent and that the best ...

Understanding commonsense causality is a unique mark of intelligence for humans. It helps people understand the principles of the real world better and benefits the decisionmaking process related to causation. For instance, commonsense causality is crucial ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

In Quality-Driven Service Composition, tasks from an ab- stract work ow are assigned to concrete services such that work ow QoS are optimized. The following three proper- ties are desirable for a corresponding algorithm. First, the run time is ideally boun ...

Large language models (LLMs) have been shown to perform better when asked to reason step-by-step before answering a question. However, it is unclear to what degree the model's final answer is faithful to the stated reasoning steps. In this paper, we perfor ...

We introduce a practical and scalable algorithm (PALMA) for solving one of the fundamental problems of multi-agent systems -- finding matches and allocations -- in unboundedly large settings (e.g., resource allocation in urban environments, mobility-on-dem ...

In many practical scenarios, users are faced with the problem of choosing the most preferred outcome from a large set of possibilities. As people are unable to sift through them manually, decisions support systems are often used to automatically find the optimal solution. A crucial requirement for such a system is to have an accurate model of the user's preferences. Studies have shown that people are usually unable to accurately state their preferences up front, but are greatly helped by seeing examples of

We consider a participatory sensing scenario where a group of private sensors observes the same phenomenon, such as air pollution. We design a novel payment mechanism that incentivizes participation and honest behavior using the peer prediction approach, i ...

We study minimal single-task peer prediction mechanisms that have limited knowledge about agents' beliefs. Without knowing what agents' beliefs are or eliciting additional information, it is not possible to design a truthful mechanism in a Bayesian-Nash se ...

Information acquisition through crowdsensing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. It requires efficient information el ...

Several logistics service providers serve a certain number of customers, geographically spread over an area of operations. They would like to coordinate their operations so as to minimize overall cost. At the same time, they would like to keep information ...

In many interactive decision making scenarios there is often no solution that satisfies all of the user's preferences. The decision process can be helped by providing explanations. Relaxations show sets of consistent preferences and, thus, indicate which p ...

We derive conditions under which a peer-consistency mechanism can be used to elicit truthful data from non-trusted rational agents when an aggregate statistic of the collected data affects the amount of their incentives to lie. Furthermore, we discuss the ...

Choosing the best out of an increasing number of options requires reliable and accurate information. As our time and resources are limited, we commonly use the experience of others in order to take decisions. Reputation mechanisms aggregate in a formal way the feedback collected from peers and compute the ``reputation'' of products, services, or providers. They enjoy huge success and are believed to be the key of the agent mediated commerce of tomorrow. Obtaining honest feedback from self-interested agents

In Distributed Constraint Satisfaction Problems, agents often desire to ﬁnd a solution while revealing as little as possible about their variables and constraints. So far, most algorithms for DisCSP do not guarantee privacy of this information. This paper describes some simple obfuscation techniques that can be used with DisCSP algorithms such as DPOP, and provide sensible privacy guarantees based on the distributed solving process without sacriﬁcing its eﬃciency.

Recommender systems based on user feedback rank items by aggregating users' ratings in order to select those that are ranked highest. Ratings are usually aggregated using a weighted arithmetic mean. However, the mean is quite sensitive to outliers and bias ...

Conversational recommenders can help users find their most preferred item among a large range of options, a task that we call preference-based search. ...

In this paper we investigate the impact of antagonism in online discussions. We define antagonism as a new class of textual opinions - direct sentiment towards the authors of previous comments. We detect the negative sentiment using aspect-based opinion mi ...

In a service-oriented e-commerce environment, it is a crucial task to help consumers choose desired products efficiently from a huge amount of dynamically configured product candidates. Decision agents can be designed to provide interactive decision aids f ...

We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We ad ...

Boi Faltings

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A Budget-balanced, Incentive-compatible Scheme for Social Choice

The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

The RADO approach to Quality-Driven Service Composition - Approximating the Pareto-Frontier in Polynomial Time

Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning

A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings

Designing Example-critiquing Interaction

Incentivizing Truthful Responses with the Logarithmic Peer Truth Serum

Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge

Orthos: A Trustworthy AI Framework for Data Acquisition

Coordinating Logistics Operations with Privacy Guarantees

Representative explanations for over-constrained problems

Peer-Prediction in the Presence of Outcome Dependent Lying Incentives

Eliminating Undesired Equilibrium Points from Incentive Compatible Reputation Mechanisms

Privacy Guarantees through Distributed Constraint Satisfaction

Rating aggregation in collaborative filtering systems

Preference-based search with adaptive recommendations

Direct Negative Opinions in Online Discussions

Agile decision agent for service-oriented e-commerce systems

Federated Learning with Bayesian Differential Privacy