A Human Memory Process Modeling

2020 ◽  
Vol 14 (2) ◽  
pp. 179-193
Author(s):  
Rahul Shrivastava ◽  
Prabhat Kumar ◽  
Sudhakar Tripathi
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Process Modeling ◽  
Sensory Information ◽  
Human Memory ◽  
Procedural Memory ◽  
Semantic Knowledge ◽  
Memory Process ◽  
Inference Mechanism ◽  
Proposed Model

Background: The cognitive models based agents proposed in the existing patents are not able to create knowledge by themselves. They also did not have the inference mechanism to take decisions and perform planning in novel situations. Objective: This patent proposes a method to mimic the human memory process for decision making. Methods: The proposed model simulates the functionality of episodic, semantic and procedural memory along with their interaction system. The sensory information activates the activity nodes which is a binding of concept and the sensory values. These activated activity nodes are captured by the episodic memory in the form of an event node. Each activity node has some participation strength in each event depending upon its involvement among other events. Recalling of events and frequent usage of some coactive activity nodes constitute the semantic knowledge in the form of associations between the activity nodes. The model also learns the actions in context to the activity nodes by using reinforcement learning. The proposed model uses an energy-based inference mechanism for planning and decision making. Results: The proposed model is validated by deploying it in a virtual war game agent and analysing the results. The obtained results show that the proposed model is significantly associated with all the biological findings and theories related to memories. Conclusion: The implementation of this model allows humanoid and game agents to take decisions and perform planning in novel situations.

Robot hand-eye cooperation based on improved inverse reinforcement learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ning Yu ◽  
Lin Nan ◽  
Tao Ku
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Visual Information ◽  
Industrial Robots ◽  
Robot Hand ◽  
Inverse Reinforcement Learning ◽  
Generative Adversarial Network ◽  
Content Type ◽  
Coordination Model ◽  
Proposed Model

Purpose How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots’ on-site decision-making ability. Design/methodology/approach The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples. Findings The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data. Originality/value A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.

scholarly journals The Missing Link Between Memory and Reinforcement Learning

2020 ◽  
Vol 11 ◽  
Author(s):  
Christian Balkenius ◽  
Trond A. Tjøstheim ◽  
Birger Johansson ◽  
Annika Wallin ◽  
Peter Gärdenfors
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Sensory Information ◽  
Sufficient Evidence ◽  
Extended Model ◽  
Missing Link ◽  
Memory Mechanism ◽  
State Action ◽  
Vicarious Trial And Error ◽  
Over Time

Reinforcement learning systems usually assume that a value function is defined over all states (or state-action pairs) that can immediately give the value of a particular state or action. These values are used by a selection mechanism to decide which action to take. In contrast, when humans and animals make decisions, they collect evidence for different alternatives over time and take action only when sufficient evidence has been accumulated. We have previously developed a model of memory processing that includes semantic, episodic and working memory in a comprehensive architecture. Here, we describe how this memory mechanism can support decision making when the alternatives cannot be evaluated based on immediate sensory information alone. Instead we first imagine, and then evaluate a possible future that will result from choosing one of the alternatives. Here we present an extended model that can be used as a model for decision making that depends on accumulating evidence over time, whether that information comes from the sequential attention to different sensory properties or from internal simulation of the consequences of making a particular choice. We show how the new model explains both simple immediate choices, choices that depend on multiple sensory factors and complicated selections between alternatives that require forward looking simulations based on episodic and semantic memory structures. In this framework, vicarious trial and error is explained as an internal simulation that accumulates evidence for a particular choice. We argue that a system like this forms the “missing link” between more traditional ideas of semantic and episodic memory, and the associative nature of reinforcement learning.

CONTEXTUALITY, INCOMPATIBILITY AND BIASED INFERENCE IN A QUANTUM-LIKE FORMULATION OF COMPUTATIONAL TRUST

2014 ◽  
Vol 17 (05) ◽  
pp. 1450020 ◽  
Author(s):  
MEHRDAD ASHTIANI ◽  
MOHAMMAD ABDOLLAHI AZGOMI
Keyword(s):  
Decision Making ◽  
Quantum Theory ◽  
Mathematical Framework ◽  
Inference Mechanism ◽  
Computational Trust ◽  
Quantum Probabilities ◽  
Proposed Model ◽  
Human Decision ◽  
Transitivity Property ◽  
Trust Models

Trust models play an important role in computational environments. One of the main aims of the work undertaken in this domain is to provide a model that can better describe the socio-technical nature of computational trust. It has been recently shown that quantum-like formulations in the field of human decision making can better explain the underlying nature of these types of processes. Based on this research, the aim of this paper is to propose a novel model of trust based on quantum probabilities as the underlying mathematics of quantum theory. It will be shown that by using this new mathematical framework, we will have a powerful mechanism to model the contextuality property of trust. Also, it is hypothesized that many events or evaluations in the context of trust can be and should be considered as incompatible, which is unique to the noncommutative structure of quantum probabilities. The main contribution of this paper will be that, by using the quantum Bayesian inference mechanism for belief updating in the framework of quantum theory, we propose a biased trust inference mechanism. This mechanism allows us to model the negative and positive biases that a trustor may subjectively feel toward a certain trustee candidate. It is shown that by using this bias, we can model and describe the exploration versus exploitation problem in the context of trust decision making, recency effects for recently good or bad transactions, filtering pessimistic and optimistic recommendations that may result in good-mouthing or bad-mouthing attacks, the attitude of the trustor toward risk and uncertainty in different situations and the pseudo-transitivity property of trust. Finally, we have conducted several experimental evaluations in order to demonstrate the effectiveness of the proposed model in different scenarios.

Conditioning and Associative Learning

2021 ◽  
Author(s):  
Alice Mason ◽  
Elliot Andrew Ludvig ◽  
Christopher R Madan
Keyword(s):  
Decision Making ◽  
Associative Learning ◽  
Operant Conditioning ◽  
Paired Associate ◽  
Human Memory ◽  
Paired Associate Learning ◽  
Memory Process ◽  
Associate Learning ◽  
Episodic Memories ◽  
Predictive Relationship

Associative learning is the process whereby humans and other animals learn the predictive relationship between cues in their environment. This process underlies simple forms of learning from rewards, such as classical and operant conditioning. In this chapter, we introduce the basics of associative learning and discuss the role that memory processes play in the establishment and maintenance of this learning. We then discuss the role that associative learning plays in human memory, including through paired associate learning, the enhancement of memory by reward, and the formation of episodic memories. Finally, we illustrate how the memory process influences choice in decision-making, where associative learning allows people to learn the values of different options. We conclude with some suggestions about how models of associative learning, memory, and choice can be integrated into a single theoretical framework.

scholarly journals Surprise acts as a reducer of outcome value in human reinforcement learning

2019 ◽  
Author(s):  
Motofumi Sumiya ◽  
Kentaro Katahira
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Simulation Analysis ◽  
Learning Task ◽  
Risk Averse ◽  
Choice Preference ◽  
Proposed Model ◽  
Modulation Parameter ◽  
Reinforcement Learning Models

Surprise occurs because of differences between a decision outcome and its predicted outcome (prediction error), regardless of whether the error is positive or negative. It has recently been postulated that surprise affects the reward value of the action outcome itself; studies have indicated that increasing surprise, as absolute value of prediction error, decreases the value of the outcome. However, how surprise affects the value of the outcome and subsequent decision making is unclear. We suggested that, on the assumption that surprise decreases the outcome value, agents will increase their risk averse choices when an outcome is often surprisal. Here, we propose the surprise-sensitive utility model, a reinforcement learning model that states that surprise decreases the outcome value, to explain how surprise affects subsequent decision-making. To investigate the assumption, we compared this model with previous reinforcement learning models on a risky probabilistic learning task with simulation analysis, and model selection with two experimental datasets with different tasks and population. We further simulated a simple decision-making task to investigate how parameters within the proposed model modulate the choice preference. As a result, we found the proposed model explains the risk averse choices in a manner similar to the previous models, and risk averse choices increased as the surprise-based modulation parameter of outcome value increased. The model fits these datasets better than the other models, with same free parameters, thus providing a more parsimonious and robust account for risk averse choices. These findings indicate that surprise acts as a reducer of outcome value and decreases the action value for risky choices in which prediction error often occurs.

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

scholarly journals Individual differences in experienced and observational decision-making illuminate interactions between reinforcement learning and declarative memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Batel Yifrah ◽  
Ayelet Ramaty ◽  
Genela Morris ◽  
Avi Mendelsohn
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Declarative Memory ◽  
Contextual Information ◽  
Memory Performance ◽  
Relevant Information ◽  
Subjective Memory ◽  
Types Of Information ◽  
Reinforcement Learning Models ◽  
Implicit And Explicit

AbstractDecision making can be shaped both by trial-and-error experiences and by memory of unique contextual information. Moreover, these types of information can be acquired either by means of active experience or by observing others behave in similar situations. The interactions between reinforcement learning parameters that inform decision updating and memory formation of declarative information in experienced and observational learning settings are, however, unknown. In the current study, participants took part in a probabilistic decision-making task involving situations that either yielded similar outcomes to those of an observed player or opposed them. By fitting alternative reinforcement learning models to each subject, we discerned participants who learned similarly from experience and observation from those who assigned different weights to learning signals from these two sources. Participants who assigned different weights to their own experience versus those of others displayed enhanced memory performance as well as subjective memory strength for episodes involving significant reward prospects. Conversely, memory performance of participants who did not prioritize their own experience over others did not seem to be influenced by reinforcement learning parameters. These findings demonstrate that interactions between implicit and explicit learning systems depend on the means by which individuals weigh relevant information conveyed via experience and observation.

scholarly journals An MCDM Model for Sustainable Decision-Making in Municipal Residential Buildings Facilities Management

2021 ◽  
Vol 13 (5) ◽  
pp. 2820
Author(s):  
Eglė Klumbytė ◽  
Raimondas Bliūdžius ◽  
Milena Medineckienė ◽  
Paris A. Fokaides
Keyword(s):  
Decision Making ◽  
Residential Buildings ◽  
Global Changes ◽  
Facilities Management ◽  
Informed Decision Making ◽  
Soviet Republic ◽  
Building Management ◽  
Proposed Model ◽  
Product Assessment ◽  
The Eu

Measuring and monitoring sustainability plays an essential role in impact assessment of global changes and development. Multi-criteria decision-making (MCDM) represents a reliable and adequate technique for assessing sustainability, especially in the field of municipal buildings management, where numerous parameters and criteria are involved. This study presents an MCDM model for the sustainable decision-making, tailored to municipal residential buildings facilities management. The main outcome of this research concerned normalized and weighted decision-making matrixes, based on the complex proportion assessment (COPRAS) and weighted aggregated sum product assessment (WASPAS) methods, applied for ranking investment alternatives related to the management of the buildings. The delivered model was applied to 20 municipal buildings of Kaunas city municipality, located in Lithuania, which an EU member state employing practices and regulations in accordance with the EU acquis, as well as a former Soviet Republic. The proposed model aspires to enhance sustainability practices in the management of municipal buildings and to demonstrate a solid tool that will allow informed decision-making in the building management sector.

Information technology support for integrated supply chain modeling

2008 ◽  
Vol 27 (1) ◽  
pp. 3-13
Author(s):  
Charu Chandra ◽  
Jānis Grabis
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Supply Chain ◽  
Process Modeling ◽  
Service Oriented Architecture ◽  
Model Development ◽  
Integrated Models ◽  
Supply Chain Modeling ◽  
Decision Making Models ◽  
Integrated Supply Chain

Multiple interrelated decision-making models are frequently used in supply chain modeling. Model integration is a precondition for efficient development and utilization of these models. This paper discusses use of modern information technology (IT) techniques and methods for integration of supply chain decision-making models. The overall approach to using IT at various stages of model development is presented. Data and process modeling techniques are used to developed semi-formalized representation of integrated models. These models support integration of decision-making components with other parts of supply chain information system. Process modeling is also used to describe interrelationships among multiple decision-making models. This representation is used as the basis for implementation of integrated models. The service-oriented architecture is proposed as an implementation platform. The presented discussion serves as the basis for further developments in developing integrated supply chain decision-making models.

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