# On the Use of the Multi-Agent Environment for Mobility Applications
## 3 Environment for Non-Spatial Interaction in Mobility Applications  
The third layer in the environment models for multi-agent systems concerns the communication environment. The communication with traffic information or guidance, with a central server or with (non-local) acquaintances, belongs to this layer. This section presents an explicitly modelled multi-agent environment, which we show can have a relevant use in this context.  
In dynamic transportation applications such as advanced traveller information systems or dial-a-ride systems, travellers, customers, and vehicles join the system nondeterministically and may also leave at any time. When specifying such open systems, the designer must define an architecture that allows for the integration of unknown agents. Newcomer agents must be able to find agents that have the properties, capabilities, or resources they need. To deal with this problem, known as the connection problem, the authors in [22] propose the concept of middle agents, which are the preferred interlocutors for agents seeking specific capabilities. The author in [23] presents recommendation systems, allowing the connection of distributed agents in open systems. This approach enables agents' gradual and distributed construction of an address book. However, in dynamic transportation systems, the desired capabilities and information sources are usually known: transportation operators, vehicles, real-time traffic information providers, etc. The problem is knowing what information these sources generate is relevant to the new agents, whose context and needs are usually constantly changing. The multi-agent environment is also used for agent matching based on the properties of the agents and the exchanged objects or messages [24].  
We adopted an environment-centric approach for agents' interaction in mobility applications for these reasons: (i) it focuses on shared data; (ii) it allows selecting relevant information without having to know or maintain knowledge about the senders of this data. We propose a generic representation of the environment, shared by all agents in the system, allowing associative discovery of other agents and exchanging information between them. Agents do not maintain an address book of others and delegate the mapping of their preferences to the properties of others to the environment. They can also describe the properties of the agents they want to interact with and the messages they want to receive. The presence of a shared environment and the ability to define complex interaction constraints make this model an excellent candidate for the design of open and dynamic transportation systems.  
This kind of shared spaces were initiated by Linda [25], which has known several extensions (e.g., Klaim [26], Mars [27] and Lime [28]). Linda-like models are based on the notion of a shared data repository. Agents communicate by exchanging tuples via an abstraction of an associative shared memory called the tuplespace. A tuplespace is a multiset of tuples (tuples duplication is allowed) and is accessed associatively (by contents) rather than by address. Every tuple is a sequence of one or more typed values. Communication in Linda is said to be _generative_: an agent _generates_ a tuple and its life cycle is independent of the agent that created it. The model presented in this section is an extension of this model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\boldsymbol{\Delta}\)**Distance** & \(\boldsymbol{\Delta}\)**Space–Time** \\ \hline Problem & \(|\)Fleet\(|\) & \(|\)Fleet\(|\) \\ \hline R1 25 customers & 64 & 53 \\ \hline C1 25 customers & 34 & 31 \\ \hline R1 50 customers & 107 & 92 \\ \hline C1 50 customers & 60 & 53 \\ \hline R1 100 customers & 181 & 150 \\ \hline C1 100 customers & 121 & 108 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results summary (criterion: fleet size).