In this paper, a novel video classification method is presented that aims to recognize different categories of third-person videos efficiently. Our motivation achieve light model could be trained with insufficient training data. With intuition, the processing 3-dimensional input broken 1D in temporal dimension on top 2D spatial. The processes related spatial frames are being done by utilizing p...

In a multi-agent system, action selection is important for the cooperation and coordination among agents. As the environment is dynamic and complex, modular Q-learning, which is one of the reinforcement learning schemes, is employed in assigning a proper action to an agent in the multi-agent system. The architecture of modular Q-learning consists of learning modules and a mediator module. The m...

Learning the new vocabulary of a new lesson by high school students before starting to teachthe whole of lesson in every session is the main concern of the writer of this study as anEnglish teacher. So the purpose of the present study is introducing some practical classroomtasks (such as, working with oxford dictionary for looking up the definitions, synonyms,antonyms and examples of new words,...

‘Pigs and people’ is a simulated environment, in which action selection mechanisms can be evaluated and compared. Action selection mechanisms attempt to solve the action selection problem faced by both animals and robots: the problem of selecting which actions to perform in order to achieve heterogeneous and possibly conflicting goals. In this project, two action selection mechanisms are implem...

This study examines the setting of canonical action research in an integrative action process within Learning by Developing (LbD) culture. The evaluation phases of this action research are based on the evaluation transactions of the Finnish Higher Education Evaluation Council (FINHEEC) and furthermore the canonical evaluation to the research is produced. Canonical is defined as “exactness” or “...

We present a system for automatically discovering action models from a set of successful observed plans. AI planning requires the definition of an action model using a language such as PDDL as input. However, building an action model from scratch is a difficult and timeconsuming task even for experts. Unlike the previous work in action-model learning, ARMS does not assume complete knowledge of ...

This paper demonstrates how A-Prolog can be used to solve the problem of non-monotonic inductive learning in the context of the learning of the behavior of dynamic domains. Non-monotonic inductive learning is an extension of traditional inductive learning, characterized by the use of default negation in the background knowledge and/or in the clauses being learned. The importance of non-monotoni...