scholarly journals Goal Recognition through Goal Graph Analysis

2001 ◽  
Vol 15 ◽  
pp. 1-30 ◽  
Author(s):  
J. Hong
Keyword(s):  
Graph Analysis ◽  
Polynomial Space ◽  
Graph Structure ◽  
Excellent Performance ◽  
Time Step ◽  
Novel Approach ◽  
Consecutive Time ◽  
Goal Recognition ◽  
Exponential Size ◽  
Goal Graph

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration

2019 ◽  
Author(s):  
Ritesh Noothigattu ◽  
Djallel Bouneffouf ◽  
Nicholas Mattei ◽  
Rachita Chandra ◽  
Piyush Madan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Ethical Values ◽  
Large Role ◽  
Learning To Learn ◽  
Inverse Reinforcement Learning ◽  
Time Step ◽  
Novel Approach

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.

scholarly journals Apply Fuzzy DEMATEL to Explore the Decisive Factors of the Auto Lighting Aftermarket Industry in Taiwan

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1187 ◽  
Author(s):  
Jing Li ◽  
Chi-Hui Wu ◽  
Chien-Wen Chen ◽  
Yi-Fen Huang ◽  
Ching-Torng Lin
Keyword(s):  
Success Factors ◽  
Technology Development ◽  
Operating Cost ◽  
Evaluation Criteria ◽  
Limited Resources ◽  
Excellent Performance ◽  
Improvement Strategy ◽  
Fuzzy Dematel ◽  
Key Success Factors ◽  
Novel Approach

Continuous improvement and innovation are solid foundations for the company to maintain excellent performance and competitive advantage. As the limited resources possessed by companies generally result in the incapability of implementing several improving plans simultaneously, researchers advocate that companies should evaluate the influential relationships among key success factors (KSFs) to explore the more dominant determinants for designing improving actions. This study focused on the auto lighting aftermarket (AM) industry in which the KSFs have not yet been adequately performed to explore the decisive criteria of an improvement strategy. After a literature review and a survey of experts, a preliminary list of suitable evaluation criteria was derived. Consequently, the fuzzy and decision-making trial and evaluation laboratory (DEMATEL) method were employed to analyze and establish the causal relationship among criteria. This study contributes to the auto lighting AM industry by using a novel approach for identifying and prioritizing the KSFs. The result indicates that product integrity was the “cause” construct on the constructs of operating cost, quality and brand, technology development, and customer satisfaction. These findings contribute to help practitioners better design effective improvement strategies.

Fully Lagrangian Modeling of Two-Phase Impulse Microjets

2013 ◽  
Author(s):  
Natalia Lebedeva ◽  
Alexander Osiptsov ◽  
Sergei Sazhin
Keyword(s):  
Carrier Phase ◽  
Stokes Equations ◽  
Order System ◽  
Navier Stokes ◽  
Time Step ◽  
Particle Trajectories ◽  
Two Phase ◽  
Novel Approach ◽  
Mesh Free ◽  
Lagrangian Modeling

A new fully Lagrangian approach to numerical simulation of 2D transient flows of viscous gas with inertial microparticles is proposed. The method is applicable to simulation of unsteady viscous flows with a dilute admixture of non-colliding particles which do not affect the carrier phase. The novel approach is based on a modification and combination of the full Lagrangian method for the dispersed phase, proposed by Osiptsov [1], and a Lagrangian mesh-free vortex-blob method for Navier-Stokes equations describing the carrier phase in the format suggested by Dynnikova [2]. In the combined numerical algorithm, both these approaches have been implemented and used at each time step. In the first stage, the vortex-blob approach is used to calculate the fields of velocity and spatial derivatives of the carrier-phase flow. In the second stage, using Osiptsov’s approach, particle velocities and number density are calculated along chosen particle trajectories. In this case, the problem of calculation of all parameters of both phases (including particle concentration) is reduced to the solution of a high-order system of ordinary differential equations, describing transient processes in both carrier and dispersed phases. The combined method is applied to simulate the development of vortex ring-like structures in an impulse two-phase microjet. This flow involves the formation of local zones of particle accumulation, regions of multiple intersections of particle trajectories, and multi-valued particle velocity and concentration fields. The proposed mesh-free approach enables one to reproduce with controlled accuracy these flow features without excessive computational costs.

scholarly journals Behavioral agent-based framework for interacting financial markets

2020 ◽  
Vol 5 (2) ◽  
pp. 94-115
Author(s):  
Heba M. Ezzat
Keyword(s):  
Financial Markets ◽  
Trading Strategy ◽  
Past Performance ◽  
Time Step ◽  
Monetary Policies ◽  
Content Type ◽  
Agent Based ◽  
Market Makers ◽  
Novel Approach ◽  
Excess Volatility

Purpose This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored. Design/methodology/approach The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders. Findings The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure. Practical implications Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability. Originality/value This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

scholarly journals Two-Step Unconditionally Stable Noniterative Dissipative Displacement Method for Analysis of Nonlinear Structural Dynamics Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Changqing Li ◽  
Baoyi Sheng ◽  
Zhipeng Lai ◽  
Lizhong Jiang ◽  
Ping Xiang
Keyword(s):  
Displacement Method ◽  
Time Step ◽  
Structural Dynamic ◽  
Unconditionally Stable ◽  
Novel Approach ◽  
Calculation Process ◽  
Dynamics Problems ◽  
And Storage ◽  
Formula Method ◽  
Method Show

When solving structural dynamic problems, the displacement algorithm needs only calculating and storing structure’s displacements in the main calculation process, which makes the displacement algorithm have advantages over multivariable algorithms in calculation efficiency and storage requirements. By using a novel approach based on dimensional analysis firstly given by the first author, a one-parameter family of two-step unconditionally stable noniterative displacement algorithms, referred to as the CQ-2x method, is developed. Compared with other unconditionally stable noniterative multivariable algorithms such as the representative KR- α method, the proposed method has advantages in several aspects. The CQ-2x method is unconditionally stable regardless of stiffness hardening or stiffness weakening, while the KR- α method is only conditionally stable in case of stiffness hardening. The CQ-2x method needs only one solver within one time step, while the KR- α method needs two solvers within one time step, which makes the CQ-2x method show higher efficiency. Numerical examples are presented to demonstrate the potential of the proposed method.

scholarly journals Counterplanning using Goal Recognition and Landmarks

2018 ◽  
Author(s):  
Alberto Pozanco ◽  
Yolanda E-Martín ◽  
Susana Fernández ◽  
Daniel Borrajo
Keyword(s):  
Automated Planning ◽  
Experimental Results ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent ◽  
Goal Recognition ◽  
Domain Independent

In non-cooperative multi-agent systems, agents might want to prevent the opponents from achieving their goals. One alternative to solve this task would be using counterplanning to generate a plan that allows an agent to block other's to reach their goals. In this paper, we introduce a fully automated domain-independent approach for counterplanning. It combines; goal recognition to infer an opponent's goal; landmarks' computation to identify subgoals that can be used to block opponents' goals achievement; and classical automated planning to generate plans that prevent the opponent's goals achievement. Experimental results in several domains show the benefits of our novel approach. 

scholarly journals Structural differences between REM and non-REM dream reports assessed by graph analysis

2020 ◽  
Author(s):  
Joshua M. Martin ◽  
Danyal Wainstein ◽  
Natalia B. Mota ◽  
Sergio A. Mota-Rolim ◽  
John Fontenele Araújo ◽  
...  
Keyword(s):  
Structural Organization ◽  
Sleep Stage ◽  
Graph Analysis ◽  
Graph Structure ◽  
Rapid Eye Movement Sleep ◽  
Connected Component ◽  
Dream Analysis ◽  
Structural Differences ◽  
Dream Report ◽  
Dream Research

AbstractDream reports collected after rapid eye movement sleep (REM) awakenings are, on average, longer, more vivid, bizarre, emotional and story-like compared to those collected after non-REM. However, a comparison of the word-to-word structural organization of dream reports is lacking, and traditional measures that distinguish REM and non-REM dreaming may be confounded by report length. This problem is amenable to the analysis of dream reports as non-semantic directed word graphs, which provide a structural assessment of oral reports, while controlling for individual differences in verbosity. Against this background, the present study had two main aims: Firstly, to investigate differences in graph structure between REM and non-REM dream reports, and secondly, to evaluate how non-semantic directed word graph analysis compares to the widely used measure of report length in dream analysis. To do this, we analyzed a set of 125 dream reports obtained from 19 participants in controlled laboratory awakenings from REM and N2 sleep. We found that: (1) graphs from REM sleep possess a larger connectedness compared to those from N2; (2) measures of graph structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity; and (3) measures of the Largest Connected Component of a graph can improve a model containing report length in predicting sleep stage and dream complexity. These results indicate that dream reports sampled after REM awakening have on average a larger connectedness compared to those sampled after N2 (i.e. words recur with a longer range), a difference which appears to be related to underlying differences in dream complexity. Altogether, graph analysis represents a promising method for dream research, due to its automated nature and potential to complement report length in dream analysis.

scholarly journals NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3228
Author(s):  
Harsh V. P. Singh ◽  
Qusay H. Mahmoud
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Situational Awareness ◽  
Forecast Accuracy ◽  
Training Data ◽  
Time Step ◽  
Design Elements ◽  
Novel Approach ◽  
Out Of Sample ◽  
Learning Machine

A novel approach presented herein transforms the Human Machine Interface (HMI) states, as a pattern of visual feedback states that encompass both operator actions and process states, from a multi-variate time-series to a natural language processing (NLP) modeling domain. The goal of this approach is to predict operator response patterns for n − a h e a d time-step window given k − l a g g e d past HMI state patterns. The NLP approach offers the possibility of encoding (semantic) contextual relations within HMI state patterns. Towards which, a technique for framing raw HMI data for supervised training using sequence-to-sequence (seq2seq) deep-learning machine translation algorithms is presented. In addition, a custom Seq2Seq convolutional neural network (CNN) NLP model based on current state-of-the-art design elements such as attention, is compared against a standard recurrent neural network (RNN) based NLP model. Results demonstrate comparable effectiveness of both the designs of NLP models evaluated for modeling HMI states. RNN NLP models showed higher ( ≈ 26 % ) forecast accuracy, in general for both in-sample and out-of-sample test datasets. However, custom CNN NLP model showed higher ( ≈ 53 % ) validation accuracy indicative of less over-fitting with the same amount of available training data. The real-world application of the proposed NLP modeling of industrial HMIs, such as in power generating stations control rooms, aviation (cockpits), and so forth, is towards the realization of a non-intrusive operator situational awareness monitoring framework through prediction of HMI states.

scholarly journals The random exploration graph for optimal exploration of unknown environments

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668711 ◽  
Author(s):  
Alfredo Toriz Palacios ◽  
Abraham Sánchez L ◽  
Jose María Enrique Bedolla Cordero
Keyword(s):  
Graph Algorithm ◽  
Graph Structure ◽  
Random Choice ◽  
Robot System ◽  
Exploration Process ◽  
Left Behind ◽  
Unknown Environments ◽  
Novel Approach ◽  
Control Concept ◽  
Time Critical

The exploration of an unknown environment by a robot system is a well-studied problem in robotics; however, although many of the proposals made in this field represent efficient tools in terms of exploration paradigm, most of them are not efficient for time critical applications since the robot may visit the same place more than once during backtracking. In this way and considering these limitations, this article presents a novel approach called the random exploration graph, which addresses the problem of exploring unknown environments by building a graph structure created incrementally by the random choice of the free frontier in the observation range of the robot. In addition, the random exploration graph algorithm uses a new concept called “frontier control” introduced in this work, used to store nodes left behind in the graph structure that have not been fully explored and that will be used to guide the exploration process in an efficient way, when the algorithm needs to go back to previously visited areas to continue exploration. The frontier control concept next to the versatility of the graph structure used for the exploration process is the main contribution of this work.

Sign in / Sign up

Export Citation Format

Share Document