Due to e-commerce growth and urbanization, delivery companies are facing a rising demand for home deliveries, which makes it increasingly challenging to provide parcel delivery that is cheap, sustainable, and on time. This challenge has motivated recent interest in crowdshipping. In crowdshipping systems, private citizens are incentivized to contribute to parcel delivery by making small detours in their daily lives. To advance crowdshipping as a new delivery paradigm, new crowdshipping concepts have to be developed, tested, and evaluated. One way to test and evaluate new crowdshipping concepts is agent-based simulation. In this paper, we present a crowdshipping simulator where the crowd workers are modeled as agents who decide autonomously whether they want to accept a delivery task. The agents' decisions can be modeled based on shipping plans, which allow to easily implement the most common behavior assumptions found in the crowdshipping literature. We perform simulation experiments for different scenarios, which demonstrate the capabilities of our simulator.
Applying Theory of Mind to multi-agent systems enables agents to model and reason about other agents’ minds. Recent work shows that this ability could increase the performance of agents, making them more efficient than agents that lack this ability. However, modelling others agents’ minds is a difficult task, given that it involves many factors of uncertainty, e.g., the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents. In this paper, we explore how agents acquire and update Theory of Mind under conditions of uncertainty. To represent uncertain Theory of Mind, we add probability estimation on a formal semantics model for agent communication based on the BDI architecture and agent communication languages.
This paper proposes the application of computational social choice mechanisms to establish cooperative behavior within traffic scenarios involving autonomous vehicles. The main aim is to understand the suitability of commonly used voting rules as a potential mechanism for collective decision making in platoon applications considering unreliable communications. To realistically assess the system performance, we designed an integrated simulation platform composed of an agent-based platform, a microscopic traffic and a vehicular network models. Results show the viability of these simple voting mechanism to maintain high satisfaction among platoon members, which that can lead to stable formations and consequently better traffic conditions. However, additional mechanisms might need to be considered for larger platoon formations to timely guarantee consensus between voters.
Recent studies have shown that applying Theory of Mind to agent technologies enables agents to model and reason about other agents’ minds, making them more efficient than agents that do not have this ability or agents that have a more limited ability of modelling others’ minds. Apart from the interesting results of combining Theory of Mind and agent technologies, an important premise has not been yet fully investigated in the AI literature: how do agents acquire and update their models of others’ minds? In the context of multi-agent systems, one of the most natural ways in which agents can acquire models of other agents’ mental attitudes is through communication. In this work, we propose an operational semantics for agents to update Theory of Mind through communication. We not only make our formalisation broadly applicable by defining a formal semantics based on components from the BDI architecture, but we also implement our approach in an agent-oriented programming language that is based on that architecture.
Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.
