1,505 research outputs found
Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior
In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad
Challenges and Solutions to Criminal Liability for the Actions of Robots and AI
Civil liability legislation is currently being developed, but little attention has been paid to the issue of criminal liability for the actions of robots. The study describes the generations of robots and points out the concerns about robots’ autonomy. The more autonomy robots obtain, the greater capacity they have for self-learning, yet the more difficulty in proving the failure foreseeability when designing and whether culpability or the elements of a specific crime can be considered. In this study, the tort liability depending on the category of robots is described, and the possible solutions are analyzed. It is shown that there is no need to introduce new criminal law constructions, but to focus on the process of proof. Instead of changing the legal system, it is necessary to create the most detailed audit trail telling about the robot’s actions and surroundings or to have a digital twin of the robot
Towards Data-driven Simulation Modeling for Mobile Agent-based Systems
Simulation modeling provides insight into how dynamic systems work. Current simulation modeling approaches are primarily knowledge-driven, which involves a process of converting expert knowledge into models and simulating them to understand more about the system. Knowledge-driven models are useful for exploring the dynamics of systems, but are handcrafted which means that they are expensive to develop and reflect the bias and limited knowledge of their creators. To address limitations of knowledge-driven simulation modeling, this dissertation develops a framework towards data-driven simulation modeling that discovers simulation models in an automated way based on data or behavior patterns extracted from systems under study. By using data, simulation models can be discovered automatically and with less bias than through knowledge-driven methods. Additionally, multiple models can be discovered that replicate the desired behavior. Each of these models can be thought of as a hypothesis about how the real system generates the observed behavior. This framework was developed based on the application of mobile agent-based systems. The developed framework is composed of three components: 1) model space specification; 2) search method; and 3) framework measurement metrics. The model space specification provides a formal specification for the general model structure from which various models can be generated. The search method is used to efficiently search the model space for candidate models that exhibit desired behavior. The five framework measurement metrics: flexibility, comprehensibility, controllability, compossability, and robustness, are developed to evaluate the overall framework. Furthermore, to incorporate knowledge into the data-driven simulation modeling framework, a method was developed that uses System Entity Structures (SESs) to specify incomplete knowledge to be used by the model search process. This is significant because knowledge-driven modeling requires a complete understanding of a system before it can be modeled, whereas the framework can find a model with incomplete knowledge. The developed framework has been applied to mobile agent-based systems and the results demonstrate that it is possible to discover a variety of interesting models using the framework
Simulated Experince Evaluation in Developing Multi-agent Coordination Graphs
Cognitive science has proposed that a way people learn is through self-critiquing by generating \u27what-if\u27 strategies for events (simulation). It is theorized that people use this method to learn something new as well as to learn more quickly. This research adds this concept to a graph-based genetic program. Memories are recorded during fitness assessment and retained in a global memory bank based on the magnitude of change in the agent’s energy and age of the memory. Between generations, candidate agents perform in simulations of the stored memories. Candidates that perform similarly to good memories and differently from bad memories are more likely to be included in the next generation. The simulation-informed genetic program is evaluated in two domains: sequence matching and Robocode. Results indicate the algorithm does not perform equally in all environments. In sequence matching, experiential evaluation fails to perform better than the control. However, in Robocode, the experiential evaluation method initially outperforms the control then stagnates and often regresses. This is likely an indication that the algorithm is over-learning a single solution rather than adapting to the environment and that learning through simulation includes a satisficing component
Artificial Intelligence and Bank Soundness: Between the Devil and the Deep Blue Sea - Part 2
Banks have experienced chronic weaknesses as well as frequent crisis over the years. As bank failures are costly and affect global economies, banks are constantly under intense scrutiny by regulators. This makes banks the most highly regulated industry in the world today. As banks grow into the 21st century framework, banks are in need to embrace Artificial Intelligence (AI) to not only to provide personalized world class service to its large database of customers but most importantly to survive. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions challenges from the main strand of CAMELS into distinct categories of AI into 1(C), 4(A), 17(M), 8 (E), 1(L), 2(S) categories that banks and regulatory teams need to consider in evaluating AI use in banks. Although AI offers numerous opportunities to enable banks to operate more efficiently and effectively, at the same time banks also need to give assurance that AI ‘do no harm’ to stakeholders. Posing many unresolved questions, it seems that banks are trapped between the devil and the deep blue sea for now
Real Islamic Logic
Four options for assigning a meaning to Islamic Logic are surveyed including
a new proposal for an option named "Real Islamic Logic" (RIL). That approach to
Islamic Logic should serve modern Islamic objectives in a way comparable to the
functionality of Islamic Finance. The prospective role of RIL is analyzed from
several perspectives: (i) parallel distributed systems design, (ii) reception
by a community structured audience, (iii) informal logic and applied
non-classical logics, and (iv) (in)tractability and artificial intelligence
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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