Learning Task[edit | edit source]
- Analyse the area of applications of swarm intelligence below!
- Explore the concept of Open Innovation Ecosystem and explain concept of swarm intelligence can be used for innovation.
Applications[edit | edit source]
Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital swarm for self-assembly and interferometry. NASA is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors. Conversely al-Rifaie and Aber have used Stochastic Diffusion Search to help locate tumours. Swarm intelligence has also been applied for data mining.
Ant-based routing[edit | edit source]
The use of Swarm Intelligence in Telecommunication Networks has also been researched, in the form of Ant Based Routing. This was pioneered separately by Dorigo et al. and Hewlett Packard in the mid-1990s, with a number of variations since. Basically this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each "ant" (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then you pay for the cinema before you know how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence (Rheingold: 2002, P175).
The location of transmission infrastructure for wireless communication networks is an important engineering problem involving competing objectives. A minimal selection of locations (or sites) are required subject to providing adequate area coverage for users. A very different ant inspired swam intelligence algorithm, Stochastic diffusion search (SDS), has been successfully used to provide a general model for this problem, related to circle packing and set covering. It has been shown that the SDS can be applied to identify suitable solutions even for large problem instances.
Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. At Southwest Airlines a software program uses swarm theory, or swarm intelligence—the idea that a colony of ants works better than one alone. Each pilot acts like an ant searching for the best airport gate. "The pilot learns from his experience what's the best for him, and it turns out that that's the best solution for the airline," Douglas A. Lawson explains. As a result, the "colony" of pilots always go to gates they can arrive at and depart from quickly. The program can even alert a pilot of plane back-ups before they happen. "We can anticipate that it's going to happen, so we'll have a gate available," Lawson says.
Crowd simulation[edit | edit source]
Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds.
Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm technology for rendering, realistically depicting the movements of groups of fish and birds using the Boids system. Tim Burton's Batman Returns also made use of swarm technology for showing the movements of a group of bats. The Lord of the Rings film trilogy made use of similar technology, known as Massive, during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.
Airlines have used swarm theory to simulate passengers boarding a plane. Southwest Airlines researcher Douglas A. Lawson used an ant-based computer simulation employing only six interaction rules to evaluate boarding times using various boarding methods.(Miller, 2010, xii-xviii).
Innovation and Teambuilding[edit | edit source]
For complex problems, that needs knowledge/expertise from different domain, the swarm is not regarded as a collection of simple and more or less equally structured individuals (like in algorithmic examples of swarm intelligence). In this context of innovation the individuals in swarm have specialities, that are donated to swarm to solve problems, that cannot be solved by a single individual alone. We can easily list the special expertise of individual of a group/swarm, but if the individuals just work for their personal benefit, then the collaborative network between group members may have an impact on the swarm performance in the context of innovation.
Learning Task[edit | edit source]
- Do concurrent systems always have a poorer performance?
- Analyse the recommendation "If you want walk fast walk alone, if you want to walk far walk together." for similarities with swarm intelligence. Can you find
- example in your personal life,
- scientific evidence and
- strategies that support swarm intelligence in multidisciplinary teams.
- case studies of diagnostic interventions in Psychology, that analyse the team climate and team building.
- What are favorable conditions for yourself, in which you would be willing to sacrifice part of your personal benefits to support collaborative goals in a swarm?
Beneficial Team Climate for Innovation[edit | edit source]
To have a scientific approach to this, the development and application of a measure of group processes and climate for innovation is necessary. Explore psychological requirements and constraints for the Team Climate Inventory (TCI). Explore the concepts of
- shared objectives or vision
- group participation in decision making and safety (appreciation especially for preliminary ideas with errors and short-comings)
- team support for innovation and the group's orientation on joint tasks in comparsion to the focus on individual benefits.
Swarmic art[edit | edit source]
In a series of works al-Rifaie et al. have successfully used two swarm intelligence algorithms – one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (stochastic diffusion search, SDS) and the other algorithm mimicking the behaviour of birds ﬂocking (particle swarm optimization, PSO) – to describe a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the ‘birds ﬂocking’ - as they seek to follow the input sketch - and the global behaviour of the "ants foraging" - as they seek to encourage the ﬂock to explore novel regions of the canvas. The "creativity" of this hybrid swarm system has been analysed under the philosophical light of the "rhizome" in the context of Deleuze’s "Orchid and Wasp" metaphor.
In a more recent work of al-Rifaie et al., "Swarmic Sketches and Attention Mechanism", introduces a novel approach deploying the mechanism of 'attention' by adapting SDS to selectively attend to detailed areas of a digital canvas. Once the attention of the swarm is drawn to a certain line within the canvas, the capability of PSO is used to produce a 'swarmic sketch' of the attended line. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic roles – attention to areas with more details – associated to them via their fitness function. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the ‘artist’ swarms embark on interpreting the input line drawings. In other works while PSO is responsible for the sketching process, SDS controls the attention of the swarm.
The "computational creativity" of the above mentioned systems are discussed in through the two prerequisites of creativity (i.e. freedom and constraints) within the swarm intelligence's two infamous phases of exploration and exploitation.
References[edit | edit source]
- Lewis, M. Anthony; Bekey, George A.. "The Behavioral Self-Organization of Nanorobots Using Local Rules". Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems.
- Identifying metastasis in bone scans with Stochastic Diffusion Search, al-Rifaie, M.M. & Aber, A., Proc. IEEE Information Technology in Medicine and Education, ITME 2012, pp. 519-523.
- al-Rifaie, Mohammad Majid, Ahmed Aber, and Ahmed Majid Oudah. "Utilising Stochastic Diffusion Search to identify metastasis in bone scans and microcalcifications on mammographs." In Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on, pp. 280-287. IEEE, 2012.
- Martens, D.; Baesens, B.; Fawcett, T. (2011). "Editorial Survey: Swarm Intelligence for Data Mining". Machine Learning 82 (1): 1–42. doi:10.1007/s10994-010-5216-5.
- Whitaker, R.M., Hurley, S.. An agent based approach to site selection for wireless networks. Proc ACM Symposium on Applied Computing, pp. 574–577, (2002).
- "Planes, Trains and Ant Hills: Computer scientists simulate activity of ants to reduce airline delays". Science Daily. April 1, 2008. Retrieved December 1, 2010.
- Miller, Peter (2010). The Smart Swarm: How understanding flocks, schools, and colonies can make us better at communicating, decision making, and getting things done. New York: Avery. ISBN 978-1-58333-390-7.
- Anderson, N., & West, M. A. (1996). The Team Climate Inventory: Development of the TCI and its applications in teambuilding for innovativeness. European Journal of work and organizational psychology, 5(1), 53-66.
- al-Rifaie, MM, Bishop, J.M., & Caines, S., Creativity and Autonomy in Swarm Intelligence Systems, Cognitive Computing 4:3, pp. 320-331, (2012).
- Deleuze G, Guattari F, Massumi B. A thousand plateaus. Minneapolis: University of Minnesota Press; 2004.
- al-Rifaie, Mohammad Majid, and John Mark Bishop. "Swarmic sketches and attention mechanism". Evolutionary and Biologically Inspired Music, Sound, Art and Design. Springer Berlin Heidelberg, 2013. 85-96.
- al-Rifaie, Mohammad Majid, and John Mark Bishop. "Swarmic paintings and colour attention". Evolutionary and Biologically Inspired Music, Sound, Art and Design. Springer Berlin Heidelberg, 2013. 97-108.
- al-Rifaie, Mohammad Majid, Mark JM Bishop, and Ahmed Aber. "Creative or Not? Birds and Ants Draw with Muscle." Proceedings of AISB'11 Computing and Philosophy (2011): 23-30.
- al-Rifaie, Mohammad Majid, Ahmed Aber and John Mark Bishop. "Cooperation of Nature and Physiologically Inspired Mechanisms in Visualisation." Biologically-Inspired Computing for the Arts: Scientific Data through Graphics. IGI Global, 2012. 31-58. Web. 22 Aug. 2013. doi:10.4018/978-1-4666-0942-6.ch003
- al-Rifaie MM, Bishop M (2013) Swarm intelligence and weak artificial creativity. In: The Association for the Advancement of Artificial Intelligence (AAAI) 2013: Spring Symposium, Stanford University, Palo Alto, California, U.S.A., pp 14–19