{"title":"17.1 AI x Robotics: Technology Challenges and Opportunities in Sensors, Actuators, and Integrated Circuits","authors":"M. Fujita","doi":"10.1109/ISSCC.2019.8662358","DOIUrl":null,"url":null,"abstract":"In 1956 at the Dartmouth conference, the terminology of artificial intelligence (AI) was first used. In those days it was also called as symbolic AI (Symbolic-AI) [1]. For example, let us assume a block world problem in Fig 17.1.1 (right), where a block is represented as a symbol, “Block1”, and it can be operated by operators such as “PICKUP(Block1). In order to apply the operator “PICKUP” to the target object, “Block1”, the AI system has to check the pre-condition such as “CLEAR Block1”, which means there is no object on “Block1”. The Fig. 17.1.1 (right) shows an example of a task from State-A to State-B. The system has to search the possible operators and the pre-conditions so that State-B is achieved. There are many basic algorithms developed in Symbolic-AI era, which are often used today including the A*-search algorithm. Shakey is the representative example of intelligent robots based on Symbolic-AI. It was a wheel-based movable robot equipped with a TV-camera, Laser-Range-Finder, etc. It can move blocks in the real world using Symbolic-AI technologies. Its behavior control architecture is shown in Fig 17.1.1 (left). It has three steps, SENSE, PLAN, and ACT. Therefore, it is known as the SENSE-PLAN-ACT architecture. It is computationally intensive especially in the PLAN computation, therefore it is difficult if the environment is dynamically changing.","PeriodicalId":265551,"journal":{"name":"2019 IEEE International Solid- State Circuits Conference - (ISSCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Solid- State Circuits Conference - (ISSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCC.2019.8662358","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In 1956 at the Dartmouth conference, the terminology of artificial intelligence (AI) was first used. In those days it was also called as symbolic AI (Symbolic-AI) [1]. For example, let us assume a block world problem in Fig 17.1.1 (right), where a block is represented as a symbol, “Block1”, and it can be operated by operators such as “PICKUP(Block1). In order to apply the operator “PICKUP” to the target object, “Block1”, the AI system has to check the pre-condition such as “CLEAR Block1”, which means there is no object on “Block1”. The Fig. 17.1.1 (right) shows an example of a task from State-A to State-B. The system has to search the possible operators and the pre-conditions so that State-B is achieved. There are many basic algorithms developed in Symbolic-AI era, which are often used today including the A*-search algorithm. Shakey is the representative example of intelligent robots based on Symbolic-AI. It was a wheel-based movable robot equipped with a TV-camera, Laser-Range-Finder, etc. It can move blocks in the real world using Symbolic-AI technologies. Its behavior control architecture is shown in Fig 17.1.1 (left). It has three steps, SENSE, PLAN, and ACT. Therefore, it is known as the SENSE-PLAN-ACT architecture. It is computationally intensive especially in the PLAN computation, therefore it is difficult if the environment is dynamically changing.