Designing and implementing AI in games is an interesting, yet complex task. This paper briefly presents some applications that make use of Answer Set Programming for such a task, and show some advantages of declarative programming frameworks against imperative (algorithmic) approaches while dealing with knowledge representation and reasoning: solid theoretical bases, no need for algorithm desig...

We present a skill learning model CLARION. Different from existing models of high-level skill learning that use a topdown approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. CLARION is formed by integrating connectionist, rei...

In this study, the L2 acquisition of English third person-s in different settings is examined. Two types knowledge are declarative as about a grammatical rule, and procedural ability to use these rules spoken production. Data on was collected from 32 young learners Sweden 44 Vietnam. The learners' grammar assessed using elicitation instruments that captured subject-verb agreement person singula...

This paper presents a skill learning model Clarion. Diierent from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops rst and declarative knowledge develops from it. Clarion which follows this approach...

In order to develop versatile cognitive agents that learn in situated contexts and generalize resulting knowledge to diierent environments , we explore the possiblity of learning both declarative and procedural knowledge in a hybrid connectionist architecture. The architecture is based on the two-level idea proposed earlier by the author. Declarative knowledge is represented symbolically, while...