Memetic Engineering as a Basis for Learning in Robotic CommunitiesThis paper represents a new contribution to the growing literature on memes. While most memetic thought has been focused on its implications on humans, this paper speculates on the role that memetics can have on robotic communities. Though speculative, the concepts are based on proven advanced multi agent technology work done at NASA - Goddard Space Flight Center and Lockheed Martin. The paper is composed of the following sections : 1) An introductory section which gently leads the reader into the realm of memes. 2) A section on memetic engineering which addresses some of the central issues with robotic learning via memes. 3) A section on related work which very concisely identifies three other areas of memetic applications, i.e., news, psychology, and the study of human behaviors. 4) A section which discusses the proposed approach for realizing memetic behaviors in robots and robotic communities. 5) A section which presents an exploration scenario for a community of robots working on Mars. 6) A final section which discusses future research which will be required to realize a comprehensive science of robotic memetics.
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Memetic Engineering For robots, memes have been defined as sets of instructions that can be followed to evolve behavior. Instructions can be encoded as written text and visible or vocal action (Silby, 2000). To allow for memetic learning, memes should also include observations of the environment. A robot that is able to observe and intelligently imitate the behavior of others is able to participate in memetic learning. In order to perform intelligent imitation, a robot needs to be able to process memetic information. This process involves evaluating models, examples, and patterns which the robot observes (Hougen et al., 2003). In addition, the robot is expected to analytically compare its current knowledge to the new information it is observing. A robot has modified its individual knowledge base when it learns a new meme. Each robot is expected to evaluate active memes in the community knowledge base for strengths and weaknesses when deciding whether to learn them. It can be expected that each individual robot will benefit from the aggregation of other robots which are also participating in memetic learning. When the community knowledge base and size expands, there is a larger selection of memes which can be evaluated and learned. With a larger community knowledge base, a robot has a larger selection of memes to modify to develop novel memes. All individuals capable of participating in memetic learning are able to generate new memes. Also, individuals who are able to broadcast observations are capable of generating new memes. The memes that are in the knowledge base of a robot are in constant competition with all other memes in the meme pool (Silby, 2000). The meme pool is the collection of all existing memes that are accessible to the other individuals in the community. An individual robot may develop new memes that become candidates for imitation in the community meme pool. The community meme pool increases with the addition of novel memes generated by individual robots in the community. The connection between an individual knowledge base and the community meme pool is similar to the structure of a distributed cloud network. A distributed cloud network is structured so that each individual is connected to the cloud where they can have limited abilities to explore large areas efficiently. NASA has shown increased interest in robotic teams for exploration and servicing. In these teams different robots would have various roles and responsibilities. To work together in a team, robots will need to exchange information on their current status, needs, capabilities and findings. Using memes as an unit of transmission could facilitate this needed information exchange. Memetics has been a recent subject of interest as a new method for information exchange (Silby 2000, Hougen et al., 2003, Aunger 2002, Blackmore 2000, Wilson et al. 2011) . In communities, memes have been studied to understand and enhance group learning (Hougen et al., 2003). Richard Dawkins first defined memes as a unit of cultural transmission (Dawkins, 1989). Essentially, memes are ideas that evolve according to the same principles as biological evolution (Silby, 2000). Memetic learning works by transmitting units of cultural ideas or symbols from one mind to another. All ideas that exist within an individual's mind are examples of memes. Memes that are good at replicating leave more copies of themselves in minds. Examples of memes are catch phrases, musical themes, scientific ideas and sayings. In robotics, examples of memes are algorithms, observations, and instructions. Figure 1 - Visual Representation of Memes Memes benefit from having behavior similar to genes (Gunders, 2010). Like genes, memes are under constant selection pressures. Memes are in constant competition to be absorbed and evolved from the collection of memes in the community. Memes are competing to be learned and those that are better at reproducing are those that are successful in accomplishing their intended behavior. Memes give communities more power and knowledge to accomplish their tasks. There are still many unknowns regarding the representation and transmission of memes in a robotic community. Memetic Engineering For robots, memes have been defined as sets of instructions that can be followed to evolve behavior. Instructions can be encoded as written text and visible or vocal action (Silby, 2000). To allow for memetic learning, memes should also include observations of the environment. A robot that is able to observe and intelligently imitate the behavior of others is able to participate in memetic learning. In order to perform intelligent imitation, a robot needs to be able to process memetic information. This process involves evaluating models, examples, and patterns which the robot observes (Hougen et al., 2003). In addition, the robot is expected to analytically compare its current knowledge to the new information it is observing. A robot has modified its individual knowledge base when it learns a new meme. Each robot is expected to evaluate active memes in the community knowledge base for strengths and weaknesses when deciding whether to learn them. It can be expected that each individual robot will benefit from the aggregation of other robots which are also participating in memetic learning. When the community knowledge base and size expands, there is a larger selection of memes which can be evaluated and learned. With a larger community knowledge base, a robot has a larger selection of memes to modify to develop novel memes. All individuals capable of participating in memetic learning are able to generate new memes. Also, individuals who are able to broadcast observations are capable of generating new memes. The memes that are in the knowledge base of a robot are in constant competition with all other memes in the meme pool (Silby, 2000). The meme pool is the collection of all existing memes that are accessible to the other individuals in the community. An individual robot may develop new memes that become candidates for imitation in the community meme pool. The community meme pool increases with the addition of novel memes generated by individual robots in the community. The connection between an individual knowledge base and the community meme pool is similar to the structure of a distributed cloud network. A distributed cloud network is structured so that each individual is connected to the cloud where they can access the knowledge bases of others in the community pool. Individuals will be able to quickly access, process, and analyze the collective knowledge within the cloud (Krutz et al., 2010). Figure 2 - Visualization of Memetic Knowledge Base Related Work Memes can be utilized for many other applications other than robotics. One example has been tracking memes to develop a coherent representation of the news cycle - the daily rhythms in the news media that have previously been difficult to perform quantitative analysis (Leskovec, Backstrom, & Kleinberg, 2008). By using memes, Leskovec et al were able to develop a framework for tracking short and distinctive phrases that travel relatively intact in media. Further, the change in information as it propagates was able to be observed. Using memetics in this model has provided a method to see how a particular idea moves within and between groups. Memetics also has the potential to enhance our study of psychology. Memes can be used to discover the origin of certain psychological conditions. By modeling the mind as a memetic construct, conditions such as depression or addiction might be explained by memetic viruses that influence the behavior of an individual (Silby, 2000). This method may provide a path to pinpoint methods to prevent the spread of malicious psychological conditions. The most powerful adaptation of memetic systems is the ability to predict behavior and evolution of future memetic structures. In the future, psychologists may be able to model memetic learning within communities and predict what will happen when individuals are exposed to combinations of memes. With this knowledge, memes will become a driving force in the study of human behavior (Silby, 2000).
