An Autonomous Agent Approach to Query Optimization in Stream Grids

2012 ◽  
pp. 276-299
Author(s):  
Saikat Mukherjee ◽  
Srinath Srinivasa ◽  
Krithi Ramamritham
Keyword(s):  
Query Optimization ◽  
Autonomous Agents ◽  
Evolutionary Game ◽  
Continuous Optimization ◽  
Autonomous Agent ◽  
Change Strategies ◽  
Stream Data ◽  
Self Interest ◽  
Grid Nodes ◽  
Intractable Problem

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

An Autonomous Agent Approach to Query Optimization in Stream Grids

2012 ◽  
pp. 407-428
Author(s):  
Saikat Mukherjee ◽  
Srinath Srinivasa ◽  
Krithi Ramamritham
Keyword(s):  
Query Optimization ◽  
Autonomous Agents ◽  
Evolutionary Game ◽  
Continuous Optimization ◽  
Autonomous Agent ◽  
Change Strategies ◽  
Stream Data ◽  
Self Interest ◽  
Grid Nodes ◽  
Intractable Problem

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

An Autonomous Agent Approach to Query Optimization in Stream Grids

2010 ◽  
Vol 1 (4) ◽  
pp. 18-39
Author(s):  
Saikat Mukherjee ◽  
Srinath Srinivasa ◽  
Krithi Ramamritham
Keyword(s):  
Query Optimization ◽  
Autonomous Agents ◽  
Evolutionary Game ◽  
Continuous Optimization ◽  
Autonomous Agent ◽  
Change Strategies ◽  
Stream Data ◽  
Self Interest ◽  
Grid Nodes ◽  
Intractable Problem

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

scholarly journals Evolution of coordination in pairwise and multi-player interactions via prior commitments

2021 ◽  
pp. 105971232199316
Author(s):  
Ndidi Bianca Ogbo ◽  
Aiman Elragig ◽  
The Anh Han
Keyword(s):  
Autonomous Agents ◽  
Evolutionary Game ◽  
Multi Agent Systems ◽  
Coordination Problem ◽  
Mutual Cooperation ◽  
Payoff Structure ◽  
Optimal Coordination ◽  
Selection Intensities ◽  
Collective Benefit ◽  
High Level

Upon starting a collective endeavour, it is important to understand your partners’ preferences and how strongly they commit to a common goal. Establishing a prior commitment or agreement in terms of posterior benefits and consequences from those engaging in it provides an important mechanism for securing cooperation. Resorting to methods from Evolutionary Game Theory (EGT), here we analyse how prior commitments can also be adopted as a tool for enhancing coordination when its outcomes exhibit an asymmetric payoff structure, in both pairwise and multi-party interactions. Arguably, coordination is more complex to achieve than cooperation since there might be several desirable collective outcomes in a coordination problem (compared to mutual cooperation, the only desirable collective outcome in cooperation dilemmas). Our analysis, both analytically and via numerical simulations, shows that whether prior commitment would be a viable evolutionary mechanism for enhancing coordination and the overall population social welfare strongly depends on the collective benefit and severity of competition, and more importantly, how asymmetric benefits are resolved in a commitment deal. Moreover, in multi-party interactions, prior commitments prove to be crucial when a high level of group diversity is required for optimal coordination. The results are robust for different selection intensities. Overall, our analysis provides new insights into the complexity and beauty of behavioural evolution driven by humans’ capacity for commitment, as well as for the design of self-organised and distributed multi-agent systems for ensuring coordination among autonomous agents.

Assessing the dynamics of knowledge transfer between masters and apprentices

2019 ◽  
Vol 36 (2) ◽  
pp. 30-32
Keyword(s):  
Knowledge Transfer ◽  
Design Methodology ◽  
Expert Knowledge ◽  
Evolutionary Game ◽  
Optimum Form ◽  
Content Type ◽  
Self Interest ◽  
Pertinent Information ◽  
Training Path ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This research paper concentrates on the deployment of asymmetric evolutionary game theory to reveal how innovative organizations best effect knowledge sharing by aligning the incentivized desire of masters to share their expert knowledge with the self-interest of apprentices who are highly motivated to accept that knowledge on an accelerated training path. These insights improve the strategic capacity of human resources teams to add value to their organization by encouraging the optimum form of knowledge transfer between masters and apprentices. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

scholarly journals Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

2020 ◽  
pp. 001872082096086 ◽  
Author(s):  
Thomas O’Neill ◽  
Nathan McNeese ◽  
Amy Barron ◽  
Beau Schelble
Keyword(s):  
Empirical Research ◽  
High Performance ◽  
Autonomous Agents ◽  
Empirical Literature ◽  
Future Research ◽  
Autonomous Agent ◽  
Research Environments ◽  
Human Autonomy ◽  
Agent Characteristics ◽  
Shared Objective

Objective We define human–autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions. Background Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human–autonomy teams (HATs) achieve success. Human–autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective. Method We searched the literature on human–autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human–autonomy teaming. We found 76 articles that met our criteria for inclusion. Results We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs. Conclusion There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.

scholarly journals Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

2021 ◽  
Vol 4 ◽  
Author(s):  
Yao Yao ◽  
Meghana Kshirsagar ◽  
Gauri Vaidya ◽  
Jens Ducrée ◽  
Conor Ryan
Keyword(s):  
Electronic Health Records ◽  
Autonomous Agents ◽  
Knowledge Integration ◽  
Clinical Decision Making ◽  
User Authentication ◽  
Clinical Decision ◽  
Knowledge Graph ◽  
Autonomous Agent ◽  
Health Records ◽  
Electronic Health

In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare.

Language evolution in apes and autonomous agents

2002 ◽  
Vol 25 (5) ◽  
pp. 622-623
Author(s):  
Angelo Cangelosi
Keyword(s):  
Dynamical System ◽  
Autonomous Agents ◽  
Language Evolution ◽  
Recent Model ◽  
Autonomous Agent ◽  
Computational Approaches ◽  
Agent Modeling ◽  
Language Origin ◽  
Language Research ◽  
Ape Language

Computational approaches based on autonomous agents share with new ape language research the same principles of dynamical system paradigms. A recent model for the evolution of symbolization and language in autonomous agents is briefly described in order to highlight the similarities between these two methodologies. The additional benefits of autonomous agent modeling in the field of language origin research are highlighted.

Blame It on the Self-Driving Car: How Autonomous Vehicles Can Alter Consumer Morality

2020 ◽  
Vol 47 (2) ◽  
pp. 272-291 ◽  
Author(s):  
Tripat Gill
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Agents ◽  
Decision Makers ◽  
The Self ◽  
Moral Judgments ◽  
Moral Norms ◽  
Attribution Of Responsibility ◽  
Policy Issues ◽  
Self Interest ◽  
Human Decision

Abstract Autonomous vehicles (AVs) are expected to soon replace human drivers and promise substantial benefits to society. Yet, consumers remain skeptical about handing over control to an AV. Partly because there is uncertainty about the appropriate moral norms for such vehicles (e.g., should AVs protect the passenger or the pedestrian if harm is unavoidable?). Building on recent work on AV morality, the current research examined how people resolve the dilemma between protecting self versus a pedestrian, and what they expect an AV to do in a similar situation. Five studies revealed that participants considered harm to a pedestrian more permissible with an AV as compared to self as the decision agent in a regular car. This shift in moral judgments was driven by the attribution of responsibility to the AV and was observed for both severe and moderate harm, and when harm was real or imagined. However, the effect was attenuated when five pedestrians or a child could be harmed. These findings suggest that AVs can change prevailing moral norms and promote an increased self-interest among consumers. This has relevance for the design and policy issues related to AVs. It also highlights the moral implications of autonomous agents replacing human decision-makers.

scholarly journals Implementing Theory of Mind on a Robot Using Dynamic Epistemic Logic

2020 ◽  
Author(s):  
Lasse Dissing ◽  
Thomas Bolander
Keyword(s):  
Theory Of Mind ◽  
Epistemic Logic ◽  
Autonomous Agents ◽  
Relevant Information ◽  
Dynamic Epistemic Logic ◽  
False Belief ◽  
Autonomous Agent ◽  
Reasoning System ◽  
Perception System ◽  
False Belief Tasks

Previous research has claimed dynamic epistemic logic (DEL) to be a suitable formalism for representing essential aspects of a Theory of Mind (ToM) for an autonomous agent. This includes the ability of the formalism to represent the reasoning involved in false-belief tasks of arbitrary order, and hence for autonomous agents based on the formalism to become able to pass such tests. This paper provides evidence for the claims by documenting the implementation of a DEL-based reasoning system on a humanoid robot. Our implementation allows the robot to perform cognitive perspective-taking, in particular to reason about the first- and higher-order beliefs of other agents. We demonstrate how this allows the robot to pass a quite general class of false-belief tasks involving human agents. Additionally, as is briefly illustrated, it allows the robot to proactively provide human agents with relevant information in situations where a system without ToM-abilities would fail. The symbolic grounding problem of turning robotic sensor input into logical action descriptions in DEL is achieved via a perception system based on deep neural networks.

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