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Despite the advancement of research and development on multi-robot teams, a key challenge still remains as to how to develop effective mechanisms that enable the robots to autonomously generate, adapt, and enhance team behaviours while improving their individual performance simultaneously. To address this issue, a cooperative learning algorithm is modified in this paper to accommodate for the individualistic...
We study three types of learning with Bayesian agent-based modeling. First, we show that previous results obtained from learning chains can be generalized to a more realistic lattice world involving multiple social interactions. Learning based on the passing of posterior probabilities converges to the truth more quickly and reliably than does learning based on imitation and sampling from the environment;...
One important role of an agent's motivational system is to choose, at any given moment, which of a number of skills the agent should attempt to improve. Many researchers have suggested “intrinsically motivated” systems that receive internal reward for model learning progress, but for the most part this notion has not been applied with respect to skill competence, or to choose between skills. In this...
In this paper we address the question of how closely everyday human teachers match a theoretically optimal teacher. We present two experiments in which subjects teach a concept to our robot in a supervised fashion. In the first experiment we give subjects no instructions on teaching and observe how they teach naturally as compared to an optimal strategy. We find that people are suboptimal in several...
“Gain-Based Separation” is a novel heuristic that modifies the standard multiclass decision tree learning algorithm to produce forests that can describe an example or object with multiple classifications. When the information gain at a node would be higher if all examples of a particular classification were removed, those examples are reserved for another tree. In this way, the algorithm performs...
Based on Brooks' subsumption architecture, we introduce a relational definition of a layered module architecture. We present two straightforward implementations following the declarative and the procedural meaning of such architectures. Based on these representations and the notion of state snapshots we then explain how to learn relational, discrete descriptions of asynchronous and possible non-deterministic...
Reinforcement learning has been commonly used in multi-robot decision making to cope with uncertainties in the environment. A shortcoming of this approach is the need for the robots to change their actions quite frequently, which is not feasible in a physical multi-robot system. This paper focuses on the development of a modified Q-learning algorithm with minimal switching of actions. By introducing...
Based on indications from the neuroscience and psychology, both perception and action can be internally simulated by activating sensor and motor areas in the brain without external sensory input or without any resulting overt behavior. This hypothesis, however, can be highly useful in the real robot applications. The robot, for instance, can cover some of the corrupted sensory inputs by replacing...
The general idea of ldquoshapingrdquo used by ethology, behavior analysis or animal training is a remarkable method. ldquoShapingrdquo is a general idea that the learner is given a reinforcement signal step by step gradually and inductively forward the behavior from easy tasks to complicated tasks. In this paper, we propose a shaping reinforcement learning method took in a general idea of shaping...
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