Description and composition of bio-inspired design patterns: a complete overview <s> Evaporation pattern <s> Animal behaviour is probabilistic. This is exemplified by the communication behaviour of ants during food-searching. Experimental evidence demonstrates that species differ in the accuracy of their recruitment. We show here, with the help of a very simple mathematical model, that the randomness of behaviour can have an adaptative advantage for ants. The model demonstrates that the degree of randomness could be optimally “tuned” to particular ecological conditions, such as food quantity and distribution. <s> BIB001 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Evaporation pattern <s> Location awareness significantly enhances the functionality of ubiquitous computing services and applications, and enriches the way they interact with users and resources in the environment. Many different alternative or complementary location sensing technologies are available. However, these technologies give location information in different formats and with different resolution and confidence. In this paper we introduce "MiddleWhere" a distributed middleware infrastructure for location that separates applications from location detection technologies. MiddleWhere enables the fusion of different location sensing technologies and facilitates the incorporation of additional location technologies on the fly as they become available. MiddleWhere utilizes probabilistic reasoning techniques to resolve conflicts and deduce the location of people given different sensor data. Besides, it allows applications to determine various kinds of spatial relationships between mobile objects and their environment, which is key in enabling a strong coupling between the physical and virtual world, as emphasized by ubiquitous computing. We have integrated MiddleWhere with our ubiquitous computing infrastructure, and have verified its flexibility and usefulness by incorporating various location sensing technologies and building a number of location-sensitive applications on top of it. <s> BIB002 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Evaporation pattern <s> Assigning tasks to agents is complex, especially in highly dynamic environments. Typical protocol-based approaches for task assignment such as Contract Net have proven their value, however, they may not be flexible enough to cope with continuously changing circumstances. In this paper we study and validate the feasibility of a field-based approach for task assignment in a complex problem domain.In particular, we apply the field-based approach for task assignment in an AGV transportation system. In this approach, transports emit fields into the environment that attract idle AGVs. To avoid multiple AGVs driving towards the same transport, AGVs emit repulsive fields. AGVs combine received fields and follow the gradient of the combined fields, that guide them towards pick locations of transports. The AGVs continuously reconsider the situation of the environment and task assignment is delayed until the load is picked, improves the flexibility of the system.Extensive experiments indicate that the field-based approach outperforms the standard Contract Net approach on various performance measures, such as the average wait time of transports and throughput. Limitations of the field-based approach are an unequal distribution of wait times across different transports and a small increase of bandwidth occupation. <s> BIB003 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Evaporation pattern <s> Abstract In this paper a method for coverage control for a convex region D ⊂ ℝ 2 in a dynamic environment is studied. An information map is introduced in which the information about each point is decaying with respect to time s.t. the robots must revisit them periodically. Also a time-varying density function is used for modeling moving points of interest. The considered gradient based control approach causes the cost function to stay within the desired bounds. But due to the non-stationary problem setup caused by the information decay it does not converge to a single point but to a bounded set, such that the robots keep gathering information continuously. With this method it is possible to gather information about several points of interest within the region D with only a few robots. In the end simulation results are presented to outline the effectiveness of the proposed control law. <s> BIB004 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Evaporation pattern <s> Pervasive computing is typically highly sensor-driven, but sensors provide only evidence of fact rather than facts themselves. The uncertainty of sensor data will affect each component in a pervasive computing system, which may decrease the quality of its provided services. We provide a general model to represent semantics of uncertainty in different levels (e.g., sensor, lower-level context and higher-level context). Within our model, fine-grained approaches are applied to evaluate and propagate uncertainties. They will help to resolve the uncertainty in each process of context management so that the effect of uncertainty on system services will be minimised. <s> BIB005 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Evaporation pattern <s> Dealing with imprecise information is a common characteristic in real-world problems. Specifically, when the source of the information are physical sensors, a level of noise in the evaluation has to be assumed. Particle Swarm Optimization is a technique that presented a good behavior when dealing with noisy fitness functions. Nevertheless, the problem is still open. In this paper we propose the use of the evaporation mechanism for managing with dynamic multi-modal optimization problems that are subject to noisy fitness functions. We will show how the evaporation mechanism does not require the detection of environment changes and how can be used for improving the performance of PSO algorithms working in noisy environments. <s> BIB006 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Evaporation pattern <s> The optimisation in dynamic and noisy environments brings closer real-world optimisation. One interesting proposal to adapt the PSO for working in dynamic and noisy environments was the incorporation of an evaporation mechanism. The evaporation mechanism avoids the detection of environment changes, providing a continuous adaptation to the environment changes and reducing the effect when the fitness function is subject to noise. However, its performance decreases when the fitness function is not subjected to noise (with respect to methods that use environment change detection). In this paper we propose a new dynamic evaporation policy to adapt the PSO algorithm to dynamic and noisy environments. Our approach improves the performance when the fitness function is dynamic and not subject to noise. It also keeps a similar performance when the fitness function is subject to noise. <s> BIB007
Evaporation is a pattern that helps dealing with dynamic environments where information used by agents can become outdated. In real world scenarios, the information appears and changes with time and its detection, prediction, or removal is usually costly or even impossible. Thus, when agents have to modify their behaviour taking into account information from the environment, information gathered recently must be more relevant than information gathered a long time ago. Evaporation is a mechanism that progressively reduces the relevance of information. Thus, recent information becomes more relevant than information processed some time ago. Evaporation was proposed as a design pattern for self-organising multi-agent systems in  ) and is usually related to Ant Colony Optimisation (ACO) (Dorig 1992) . Aliases: evaporation is also known as decay BIB004 , temporal degradation function BIB005 or freshness BIB002 . Problem: outdated information cannot be detected and it needs to be removed, or its detection involves a cost that needs to be avoided. Agent decisions rely on the freshness of the information presented in the system, enabling correct responses to dynamic environments. Solution: evaporation is a mechanism that periodically reduces the relevance of information. Thus, recent information becomes more relevant than older information. Inspiration: evaporation is present in nature. For instance, in ant colonies BIB001 , when ants deposit pheromones in the environment, these pheromones attract other ants and drive their movements from the nest to the food and vice-versa. Evaporation acts over the pheromones reducing their concentration along the time until they disappear. This mechanism allows the ants to find the shortest path to the food, even when environment changes occur (such as, new food locations or obstacles in the path). Ants are able to find the new shortest paths by discarding the old paths. Forces: evaporation is controlled by the parameters evaporation factor (i.e. how much the information is evaporated) and the evaporation frequency (i.e. frequency of evaporation execution), used to decrement the relevance of the information. The evaporation factor and evaporation frequency must deal with the dynamics of the environment: if evaporation is too fast, we may lose information; if evaporation is too slow, the information may become outdated and misguide the agents' behaviour. A higher evaporation factor releases memory, but also reduces the information available in the system for the agents. When the evaporation is applied to collaborative search or optimisation algorithms, the evaporation factor controls the balance between exploration and exploitation: high evaporation rates reduce agents' knowledge about the environment, increasing the exploration, and producing fast adaptation to environment changes. However, a higher evaporation factor decreases the performance when no environment changes occur (due to an excess of exploration). Entities-Dynamics-Environment: evaporation can be applied to any information present in the system. Periodically, its relevance decays over time. Thus, recent information becomes more relevant than information processed some time ago. Evaporation is performed by the agent or infrastructural agent periodically executing Transition Rule (3).  The rule affects the relevance value contained in C applying the function that can, for instance, impose that Rel C' = Rel C * Ev factor with Ev factor 2 ½0; . . .; 1 or that Rel C' = Rel C -Ev factor . The requirement for ðCÞ is that the relevance value decreases with the application of the rule. Implementation: the Evaporation Pattern is executed by an agent that needs to update the relevance of its internal information, or by infrastructural agents that change the relevance of the information deposited in an environment. We distinguish two approaches. In the first approach, an agent encapsulates the information and decays its own relevance. In this case, the agent follows the flow chart 7a and the corresponding interaction diagram 7b. In the second approach, the information is deposited by one agent in a host and an infrastructural agent interacts with the host to decay the information's relevance. The host provides an interface for reading and changing the relevance value. In this case, the interaction between the infrastructural agent and the host is shown in Fig. 7c . Known uses: evaporation has been used mainly in Dynamic Optimisation. Examples of algorithms using evaporation are ACO  and Quantum Swarm Optimisation Evaporation (QSOE) BIB006 . In some other works, evaporation is performed using a parameter called freshness associated to the information BIB003 . Consequences: evaporation enables adaptation to environmental changes. However, the use of evaporation in static scenarios may decrease the performance, due to the loss of information associated to this mechanism. The Evaporation Pattern provides the ability of self-adapting to environmental changes increasing the tolerance to noise, as shown in BIB007 . Related Patterns: the Evaporation Pattern is used by higher level patterns such as Digital Pheromone Pattern (Sect. 5.2.2) or Gradient Pattern (Sect. 5.2.1).