In this paper, we examine the motivations for research on cognitive architectures and review
some candidates that have been explored in the literature. After this, we consider the …

Intelligent agents are employed as the central characters in this introductory text. Beginning
with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to …

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Page 4 Knowledge Management Systems and Processes Second Edition Irma Becerra-Fernandez …

AI planning requires the definition of action models using a formal action and plan
description language, such as the standard Planning Domain Definition Language (PDDL) …

In this article, we work towards the goal of develo** agents that can learn to act in complex
worlds. We develop a probabilistic, relational planning rule representation that compactly …

Relational reinforcement learning is presented, a learning technique that combines
reinforcement learning with relational learning or inductive logic programming. Due to the …

Automated planning requires action models described using languages such as the
Planning Domain Definition Language (PDDL) as input, but building action models from …

We consider the problem of learning action models for planning in unknown stochastic
environments that can be defined using the Probabilistic Planning Domain Description …

Agents learning to act autonomously in real-world domains must acquire a model of the
dynamics of the domain in which they operate. Learning domain dynamics can be …

Reinforcement learning, and Q-learning in particular, encounter two major problems when
dealing with large state spaces. First, learning the Q-function in tabular form may be …