Eco-evolutionary dynamics on deformable fitness landscapes

Conventional approaches to modelling ecological dynamics often do not include evolutionary changes in the genetic makeup of component species and, conversely, conventional approaches to modelling evolutionary changes in the genetic makeup of a population often do not include ecological dynamics. But recently there has been considerable interest in understanding the interaction of evolutionary and ecological dynamics as coupled processes. However, in the context of complex multi-species ecosytems, especially where ecological and evolutionary timescales are similar, it is difficult to identify general organising principles that help us understand the structure and behaviour of complex ecosystems. Here we introduce a simple abstraction of coevolutionary interactions in a multi-species ecosystem. We model non-trophic ecological interactions based on a continuous but low-dimensional trait/niche space, where the location of each species in trait space affects the overlap of its resource utilisation with that of other species. The local depletion of available resources creates, in effect, a deformable fitness landscape that governs how the evolution of one species affects the selective pressures on other species. This enables us to study the coevolution of ecological interactions in an intuitive and easily visualisable manner. We observe that this model can exhibit either of the two behavioural modes discussed in the literature; namely, evolutionary stasis or Red Queen dynamics, i.e., continued evolutionary change. We find that which of these modes is observed depends on the lag or latency between the movement of a species in trait space and its effect on available resources. Specifically, if ecological change is nearly instantaneous compared to evolutionary change, stasis results; but conversely, if evolutionary timescales are closer to ecological timescales, such that resource depletion is not instantaneous on evolutionary timescales, then Red Queen dynamics result. We also observe that in the stasis mode, the overall utilisation of resources by the ecosystem is relatively efficient, with diverse species utilising different niches, whereas in the Red Queen mode the organisation of the ecosystem is such that species tend to clump together competing for overlapping resources. These models thereby suggest some basic conditions that influence the organisation of inter-species interactions and the balance of individual and collective adaptation in ecosystems, and likewise they also suggest factors that might be useful in engineering artificial coevolution

Dentocraniofacial morphology of 21 patients with unilateral cleft lip and palate: a cephalometric study.

To assess the skeletal and dental craniofacial proportions of unilateral cleft lip and palate patients who were operated upon using the Malek technique, and compare them with a normal group to highlight the effect of surgical correction on craniofacial development during growth.Comparative StudyJournal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Training Agents to Perform Sequential Behavior

This article is concerned with training an agent to perform sequential behavior. In previous work, we have been applying reinforcement learning techniques to control a reactive agent. Obviously, a purely reactive system is limited in the kind of interactions it can learn. In particular, it can learn what we call pseudosequences—that is, sequences of actions in which each action is selected on the basis of current sensory stimuli. It cannot learn proper sequences, in which actions must be selected also on the basis of some internal state. Moreover, it is a result of our research that effective learning of proper sequences is improved by letting the agent and the trainer communicate. First, we consider trainer-to-agent communication, introducing the concept of reinforcement sensor, which lets the learning robot explicitly know whether the last reinforcement was a reward or a punishment. We also show how the use of this sensor makes error recovery rules emerge. Then we introduce agent-to-trainer communication, which is used to disambiguate ambiguous training situations—that is, situations in which the observation of the agent’s behavior does not provide the trainer with enough information to decide whether the agent’s move is right or wrong. We also show an alternative solution to the problem of ambiguous situations, which involves learning to coordinate behavior in a simpler, unambiguous setting and then transferring what has been learned to a more complex situation. All the design choices we make are discussed and compared by means of experiments in a simulated world. © 1994, Sage Publications. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe