Interactive analogical retrieval: practice, theory and technology

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.PhDCommittee Chair: Goel, Ashok; Committee Member: Kolodner, Janet; Committee Member: Maher, Mary Lou; Committee Member: Nersessian, Nancy; Committee Member: Yen, Jeannett

After 150 years of watching: is there a need for synthetic ethology?

The Darwinian idea of mental continuity is about 150 years old. Although nobody has strongly denied this evolutionary link, both conceptually and practically, relative slow advance has been made by ethology and comparative psychology to quantify mental evolution. Debates on the mechanistic interpretation of cognition often struggle with the same old issues (e.g., associationism vs cognitivism), and in general, experimental methods have made also relative slow progress since the introduction of the puzzle box. In this paper, we illustrate the prevailing issues using examples on ‘mental state attribution’ and ‘perspective taking” and argue that the situation could be improved by the introduction of novel methodological inventions and insights. We suggest that focusing on problem-solving skills and constructing artificial agents that aim to correspond and interact with biological ones, may help to understand the functioning of the mind. We urge the establishment of a novel approach, synthetic ethology, in which researchers take on a practical stance and construct artificial embodied minds relying of specific computational architectures the performance of which can be compared directly to biological agents

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Multi-robot taboo-list exploration of unknown structured environments

International audienceThis paper presents a new taboo-list approach for multi-robot exploration of unknown structured environments, in which agents are implicitly guided in their navigation on a globally shared map. Agents have a local view of their environment, inside which they navigate in a asynchronous manner. When the exploration is complete, agents gather at a rendezvous point. The novelty consists in using a distributed exploration algorithm which is not guided by frontiers to perform this task. Using the Brick&Mortar Improved ant-algorithm as a base, we add robot-perspective vision, variable vision range, and an optimization which prevents agents from going to the rendezvous point before exploration is complete. The algorithm was evaluated in simulation on a set of standard maps

Priority-based human-swarm interaction applied to a foraging application

A robot swarm employs a large number of robots to facilitate sophisticated problem-solving as well as improved load-balancing and time efficiency compared to single robot systems. Swarm Intelligence is based on the local interactions between agents of the swarm that enables the emergence of a desired global behaviour, thus allowing the swarm to be autonomous. While autonomy is efficient for straightforward applications, in complex problems and environments human intervention may be more efficient. Human control of a swarm remains an open problem with multiple approaches proposed, each designed for a specific type of application. This work suggests a priority-based approach inspired by well known Human-Swarm Interaction techniques. The approach aims to serve as a high-level guide for the agents of a swarm, allowing them to use Swarm Intelligence on a low level. It also allows the division of the swarm into subswarms that can be easily controlled separately. Before experiments could be carried out to validate the proposed approach, the robots used in our experiments had to be put together, and their software needed to be designed to put to use their various components. A vision system upon which their sensing is dependent needed to be established, with defined visual markers and obstacle detection. This thesis tests the proposed priority-based Human-Swarm Interaction system by implementing a simple foraging application, using simulated and real robots, and studying the effects of introducing such a system to a group of robots that use simple Swarm Intelligence. Results show that the proposed approach does succeed in dividing the swarm into subswarms and increasing the efficiency of the foraging solution, however, some drawbacks manifested themselves throughout the process. We discuss these advantages and issues as well as future work

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

MULTI-AGENT SOURCE LOCALIZATION USING PASSIVE SENSING

Ph.DDOCTOR OF PHILOSOPH

Verification of the emergence in an architecture for multi-robot systems (AMEB)

This article analyzes the emerging behavior of a multi-robot system managed by an architecture structured in three layers: the first provides local support to the robot, manages its processes of action, perception and communication, as well as its behavioral aspect, which considers the reactive, cognitive and social aspects of the robot. In addition, it introduces an affective component that influences its behavior and the way it relates to the environment and to the other individuals in the system, based on an emotional model that takes into account fourArticle history:Received 12 September 2018Accepted 08 November 2018A Gil, pertenece al Laboratorio de Prototipos en la Universidad Nacional Experimental del Táchira y a Tepuy R+D Group. Artificial Intelligence Software Development. Mérida, Venezuela (email: [email protected])basic emotions. The second provides support to the collective processes of the system, based on the concept of emerging coordination. The latter is responsible for knowledge management and learning processes, both individually and collectively, in the system. In this article the metrics are defined to verify the emergency in the system, by means of the use of a method of verification of emergent behaviors based on Fuzzy Cognitive Maps