Patrick Hammer

Patrick Hammer
Temple University | TU · Department of Computer and Information Science

About

23
Publications
3,559
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
103
Citations
Introduction
Patrick Hammer currently works at the Department of Computer and Information Science, Temple University. Patrick does research in Artificial Intelligence. Their current project is 'NARS : an Artificial General Intelligence Project '.

Publications

Publications (23)
Article
Full-text available
A cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience. This involves managing an agent's goals as well as continuously relating sensory information to these in its perception-cognition information processing st...
Preprint
Full-text available
A cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience. This involves managing an agent's goals as well as continuously relating sensory information to these in its perception-cognition information stack. The mo...
Preprint
This work explore the possibility to combine the Jason reasoning cycle with a Non-Axiomatic Reasoning System (NARS) to develop multi-agent systems that are able to reason, deliberate and plan when information about plans to be executed and goals to be pursued is missing or incomplete. The contribution of this work is a method for BDI agents to crea...
Preprint
This work explore the possibility to combine the Jason reasoning cycle with a Non-Axiomatic Reasoning System (NARS) to develop multi-agent systems that are able to reason, deliberate and plan when information about plans to be executed and goals to be pursued is missing or incomplete. The contribution of this work is a method for BDI agents to crea...
Conference Paper
Full-text available
Using a visual scene object tracker and a Non-Axiomatic Reasoning System we demonstrate how to predict and detect various anomaly classes. The approach combines an object tracker with a base ontology and the OpenNARS reasoner to learn to classify scene regions based on accumulating evidence from typical entity class (tracked object) behaviours. The...
Chapter
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...
Chapter
This paper discuses attentional control mechanism of several systems in context of Artificial General Intelligence. Attentional control mechanism of OpenNARS, an implementation of Non-Axiomatic Reasoning System for research purposes is being introduced with description of the related functions and demonstration examples. Paper also implicitly compa...
Chapter
Full-text available
A pragmatic design for a general purpose reasoner incorporating the Non-Axiomatic Logic (NAL) and Non-Axiomatic Reasoning System (NARS) theory. The architecture and attentional control differ in many respects to the OpenNARS implementation. Key changes include; an event driven control process, separation of sensorimotor from semantic inference and...
Conference Paper
A pragmatic design for a general purpose reasoner incorporating the Non-Axiomatic Logic (NAL) and Non-Axiomatic Reasoning System (NARS) theory. The architecture and attentional control differ in many respects to the OpenNARS implementation. Key changes include ; an event driven control process, separation of sensorimotor from semantic inference and...
Conference Paper
Full-text available
Using a proprietary visual scene object tracker and the Open-NARS reasoning system we demonstrate how to predict and detect various anomaly classes. The approach combines an object tracker with a base ontology and the OpenNARS reasoning system to learn to classify scene regions based on accumulating evidence from typical entity class (tracked objec...
Chapter
This paper describes Adaptive Neuro-Symbolic Network Agent, a new design of a sensorimotor agent that adapts to its environment by building concepts based on Sparse Distributed Representations of sensorimotor sequences. Utilizing Non-Axiomatic Reasoning System theory, it is able to learn directional correlative links between concept activations tha...
Chapter
Full-text available
A novel method of Goal-directed Procedure Learning is presented that overcomes some of the drawbacks of the traditional approaches to planning and reinforcement learning. The necessary principles for acquiring goal-dependent behaviors, and the motivations behind this approach are explained. A concrete implementation exists in a Non-Axiomatic Reason...
Chapter
Full-text available
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 relation...
Chapter
Full-text available
This paper argues that according to the relevant discoveries of cognitive science, in AGI systems perception should be subjective, active, and unified with other processes. This treatment of perception is fundamentally different from the mainstream approaches in computer vision and machine learning, where perception is taken to be objective, passiv...
Article
Full-text available
This article describes and discusses the self-related mechanisms of a general-purpose intelligent system, NARS. This system is designed to be adaptive and to work with insufficient knowledge and resources. The system’s various cognitive functions are uniformly carried out by a central reasoning-learning process following a “non-axiomatic” logic. Th...
Conference Paper
Full-text available
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, ta...
Conference Paper
The introduction of Temporal Concepts into a Syllogistic based reasoning system such as NARS (Non-Axiomatic Reasoning System) provides a generalized temporal induction capability and extends the meaning of semantic relationship to include temporality.
Conference Paper
Full-text available
This paper explains the conceptual design and experimental implementation of the components of NARS that are directly related to emotion. It is argued that emotion is necessary for an AGI system that has to work with insufficient knowledge and resources. This design is also compared to the other approaches in AGI research, as well as to the relevan...
Conference Paper
This paper describes the implementation of a Non-Axiomatic Reasoning System (NARS), a unified AGI system which works under the assumption of insufficient knowledge and resources (AIKR). The system's architecture, memory structure, inference engine, and control mechanism are described in detail.
Conference Paper
Full-text available
This paper discusses several key issues in temporal and causal inference in the context of AGI. The main conclusions are: (1) the representation of temporal information should take multiple forms; (2) classical conditioning can be carried out as temporal inference; (3) causal inference can be realized without a predefined causal relation.
Conference Paper
Full-text available
This paper analyzes the assumptions of the decision making models in the context of artificial general intelligence (AGI). It is argued that the traditional approaches, exemplified by decision theory and reinforcement learning, are inappropriate for AGI, because their fundamental assumptions on available knowledge and resource cannot be satisfied h...

Network

Cited By

Projects

Projects (2)
Project
to uniformly explain and reproduce many cognitive facilities, including reasoning, learning, planning, reacting, perceiving, categorizing, prioritizing, remembering, decision making, and so on. See https://sites.google.com/site/narswang/