This article discusses an approach to add perception functionality to a general-purpose intellige... more This article discusses an approach to add perception functionality to a general-purpose intelligent system, NARS. Differently from other AI approaches toward perception, our design is based on the following major opinions: (1) Perception primarily depends on the perceiver, and subjective experience is only partially and gradually transformed into objective (intersubjective) descriptions of the environment; (2) Perception is basically a process initiated by the perceiver itself to achieve its goals, and passive receiving of signals only plays a supplementary role; (3) Perception is fundamentally unified with cognition, and the difference between them is mostly quantitative, not qualitative. The directly relevant aspects of NARS are described to show the implications of these opinions in system design, and they are compared with the other approaches. Based on the research results of cognitive science, it is argued that the Narsian approach better fits the need of perception in Artific...
AGI systems should be able to pursue their many goals autonomously while operating in realistic e... more AGI systems should be able to pursue their many goals autonomously while operating in realistic environments which are complex, dynamic, and often novel. This paper discusses the theory and mechanisms for goal generation and management in Non-Axiomatic Reasoning System (NARS). NARS works to accomplish its goals by performing executable actions while integrating feedback from its experience to build subjective, but useful, predictive and meaningful models. The system's ever-changing knowledge allows it to adaptively derive new goals from its existing goals. Derived goals not only serve to accomplish their parent goals but also represent independent motivation. The system determines how and when to pursue its many goals based on priority, context, and knowledge acquired from its experience and reasoning capabilities.
Abstract. While the fields of artificial intelligence (AI) and cognitive science (CogSci) both or... more Abstract. While the fields of artificial intelligence (AI) and cognitive science (CogSci) both originated from a deep interest in the same phe-nomenon – intelligence – and both setting themselves high aims in their early days, each has since greatly narrowed its focus, and all but aban-doned their core subject for a more limited version of the phenomenon. The many non-obvious causes for this change over the decades are per-haps understandable, but they have significantly reduced the potential of both fields to impact our understanding of the fundamentals of intelli-gence – in the wild and in the laboratory. This position paper argues that researchers in the field of artificial general intelligence (AGI) should care-fully posit their research objectives and methodology to avoid repeating the same mistakes. 1 The Big Picture of Intelligence and Cognition
This paper compares the various conceptions of “real-time” in the context of AI, as different way... more This paper compares the various conceptions of “real-time” in the context of AI, as different ways of taking the processing time into consideration when problems are solved. An architecture of real-time reasoning and learning is introduced, which is one aspect of the AGI system NARS. The basic idea is to form problem-solving processes flexibly and dynamically at run time by using inference rules as building blocks and incrementally self-organizing the system’s beliefs and skills, under the restriction of time requirements of the tasks. NARS is designed under the Assumption of Insufficient Knowledge and Resources, which leads to an inherent ability to deal with varying situations in a timely manner.
NARS is a general-purpose reasoning system that is designed to be adaptive and capable of working... more NARS is a general-purpose reasoning system that is designed to be adaptive and capable of working with insufficient knowledge and resources. This, together with its ability to deal with uncertainty, qualifies it to be used as a core component of autonomous systems. Such systems often face novel situations they were not prepared for. Hence, a certain capability to adapt and improvise can be beneficial. NARS is capable of both, autonomous learning, and decision-making without requiring supervision. The purpose of this demo is to show these capabilities using our latest NARS implementation, OpenNARS v1.6.5. The examples we introduce involve unsupervised categorization and recognition of patterns (see [8]) in sensory channels, knowledge acquisition and procedural learning based on a stream of experience, and goal-oriented behaviours based on learned knowledge. Also, aspects of the implications of autonomous decisionmaking under the Assumption of Insufficient Knowledge and Resources (AIK...
This paper describes the self-awareness and self-control mechanisms of a general-purpose intellig... more This paper describes the self-awareness and self-control mechanisms of a general-purpose intelligent system, NARS. The system perceives its internal environment basically in the same way as how it perceives its external environment, though the sensors involved are completely different. NARS uses a “self” concept to organize its relevant beliefs, tasks, and operations. The concept has an innate core, though its content and structure are mostly acquired gradually from the system’s experience. The “self” concept and its ingredients play important roles in the control of the system.
2012 15th International Conference on Information Fusion, 2012
We challenge the validity of Dempster-Shafer Theory by using an emblematic example to show that D... more We challenge the validity of Dempster-Shafer Theory by using an emblematic example to show that DS rule produces counter-intuitive result. Further analysis reveals that the result comes from a understanding of evidence pooling which goes against the common expectation of this process. Although DS theory has attracted some interest of the scientific community working in information fusion and artificial intelligence, its validity to solve practical problems is problematic, because it is not applicable to evidences combination in general, but only to a certain type situations which still need to be clearly identified.
Springer Series in Cognitive and Neural Systems, 2019
Logic should return its focus to valid reasoning in real-world situations. Since classical logic ... more Logic should return its focus to valid reasoning in real-world situations. Since classical logic only covers valid reasoning in a highly idealized situation, there is a demand for a new logic for everyday reasoning that is based on more realistic assumptions, while still keeps the general, formal, and normative nature of logic. NAL (Non-Axiomatic Logic) is built for this purpose, which is based on the assumption that the reasoner has insufficient knowledge and resources with respect to the reasoning tasks to be carried out. In this situation, the notion of validity has to be re-established, and the grammar rules and inference rules of the logic need to be designed accordingly. Consequently, NAL has features very different from classical logic and other non-classical logics, and it provides a coherent solution to many problems in logic, artificial intelligence, and cognitive science.
This article systematically analyzes the problem of defining “artificial intelligence.” It starts... more This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.
Emotions play a crucial role in different cognitive functions, such as action selection and decis... more Emotions play a crucial role in different cognitive functions, such as action selection and decision-making processes. This paper describes a new appraisal model for the emotion mechanism of NARS, an AGI system. Different from the previous appraisal model where emotions are triggered by the specific context, the new appraisal evaluates the relations between the system and its goals, based on a new set of criteria, including desirability, belief, and anticipation. Our work focuses on the functions of emotions and how emotional reactions could help NARS to improve its various cognitive capacities.
This article discusses an approach to add perception functionality to a general-purpose intellige... more This article discusses an approach to add perception functionality to a general-purpose intelligent system, NARS. Differently from other AI approaches toward perception, our design is based on the following major opinions: (1) Perception primarily depends on the perceiver, and subjective experience is only partially and gradually transformed into objective (intersubjective) descriptions of the environment; (2) Perception is basically a process initiated by the perceiver itself to achieve its goals, and passive receiving of signals only plays a supplementary role; (3) Perception is fundamentally unified with cognition, and the difference between them is mostly quantitative, not qualitative. The directly relevant aspects of NARS are described to show the implications of these opinions in system design, and they are compared with the other approaches. Based on the research results of cognitive science, it is argued that the Narsian approach better fits the need of perception in Artific...
AGI systems should be able to pursue their many goals autonomously while operating in realistic e... more AGI systems should be able to pursue their many goals autonomously while operating in realistic environments which are complex, dynamic, and often novel. This paper discusses the theory and mechanisms for goal generation and management in Non-Axiomatic Reasoning System (NARS). NARS works to accomplish its goals by performing executable actions while integrating feedback from its experience to build subjective, but useful, predictive and meaningful models. The system's ever-changing knowledge allows it to adaptively derive new goals from its existing goals. Derived goals not only serve to accomplish their parent goals but also represent independent motivation. The system determines how and when to pursue its many goals based on priority, context, and knowledge acquired from its experience and reasoning capabilities.
Abstract. While the fields of artificial intelligence (AI) and cognitive science (CogSci) both or... more Abstract. While the fields of artificial intelligence (AI) and cognitive science (CogSci) both originated from a deep interest in the same phe-nomenon – intelligence – and both setting themselves high aims in their early days, each has since greatly narrowed its focus, and all but aban-doned their core subject for a more limited version of the phenomenon. The many non-obvious causes for this change over the decades are per-haps understandable, but they have significantly reduced the potential of both fields to impact our understanding of the fundamentals of intelli-gence – in the wild and in the laboratory. This position paper argues that researchers in the field of artificial general intelligence (AGI) should care-fully posit their research objectives and methodology to avoid repeating the same mistakes. 1 The Big Picture of Intelligence and Cognition
This paper compares the various conceptions of “real-time” in the context of AI, as different way... more This paper compares the various conceptions of “real-time” in the context of AI, as different ways of taking the processing time into consideration when problems are solved. An architecture of real-time reasoning and learning is introduced, which is one aspect of the AGI system NARS. The basic idea is to form problem-solving processes flexibly and dynamically at run time by using inference rules as building blocks and incrementally self-organizing the system’s beliefs and skills, under the restriction of time requirements of the tasks. NARS is designed under the Assumption of Insufficient Knowledge and Resources, which leads to an inherent ability to deal with varying situations in a timely manner.
NARS is a general-purpose reasoning system that is designed to be adaptive and capable of working... more NARS is a general-purpose reasoning system that is designed to be adaptive and capable of working with insufficient knowledge and resources. This, together with its ability to deal with uncertainty, qualifies it to be used as a core component of autonomous systems. Such systems often face novel situations they were not prepared for. Hence, a certain capability to adapt and improvise can be beneficial. NARS is capable of both, autonomous learning, and decision-making without requiring supervision. The purpose of this demo is to show these capabilities using our latest NARS implementation, OpenNARS v1.6.5. The examples we introduce involve unsupervised categorization and recognition of patterns (see [8]) in sensory channels, knowledge acquisition and procedural learning based on a stream of experience, and goal-oriented behaviours based on learned knowledge. Also, aspects of the implications of autonomous decisionmaking under the Assumption of Insufficient Knowledge and Resources (AIK...
This paper describes the self-awareness and self-control mechanisms of a general-purpose intellig... more This paper describes the self-awareness and self-control mechanisms of a general-purpose intelligent system, NARS. The system perceives its internal environment basically in the same way as how it perceives its external environment, though the sensors involved are completely different. NARS uses a “self” concept to organize its relevant beliefs, tasks, and operations. The concept has an innate core, though its content and structure are mostly acquired gradually from the system’s experience. The “self” concept and its ingredients play important roles in the control of the system.
2012 15th International Conference on Information Fusion, 2012
We challenge the validity of Dempster-Shafer Theory by using an emblematic example to show that D... more We challenge the validity of Dempster-Shafer Theory by using an emblematic example to show that DS rule produces counter-intuitive result. Further analysis reveals that the result comes from a understanding of evidence pooling which goes against the common expectation of this process. Although DS theory has attracted some interest of the scientific community working in information fusion and artificial intelligence, its validity to solve practical problems is problematic, because it is not applicable to evidences combination in general, but only to a certain type situations which still need to be clearly identified.
Springer Series in Cognitive and Neural Systems, 2019
Logic should return its focus to valid reasoning in real-world situations. Since classical logic ... more Logic should return its focus to valid reasoning in real-world situations. Since classical logic only covers valid reasoning in a highly idealized situation, there is a demand for a new logic for everyday reasoning that is based on more realistic assumptions, while still keeps the general, formal, and normative nature of logic. NAL (Non-Axiomatic Logic) is built for this purpose, which is based on the assumption that the reasoner has insufficient knowledge and resources with respect to the reasoning tasks to be carried out. In this situation, the notion of validity has to be re-established, and the grammar rules and inference rules of the logic need to be designed accordingly. Consequently, NAL has features very different from classical logic and other non-classical logics, and it provides a coherent solution to many problems in logic, artificial intelligence, and cognitive science.
This article systematically analyzes the problem of defining “artificial intelligence.” It starts... more This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.
Emotions play a crucial role in different cognitive functions, such as action selection and decis... more Emotions play a crucial role in different cognitive functions, such as action selection and decision-making processes. This paper describes a new appraisal model for the emotion mechanism of NARS, an AGI system. Different from the previous appraisal model where emotions are triggered by the specific context, the new appraisal evaluates the relations between the system and its goals, based on a new set of criteria, including desirability, belief, and anticipation. Our work focuses on the functions of emotions and how emotional reactions could help NARS to improve its various cognitive capacities.
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