Obligation Norm Identification in Agent Societies

Most works on norms have investigated how norms are regulated using institutional mechanisms. Very few works have focused on how an agent may infer the norms of a society without the norm being explicitly given to the agent. This paper describes a mechanism for identifying one type of norm, an obligation norm. The Obligation Norm Inference (ONI) algorithm described in this paper makes use of an association rule mining approach to identify obligation norms. Using agent based simulation of a virtual restaurant we demonstrate how an agent can identify the tipping norm. The experiments that we have conducted demonstrate that an agent in the system is able to add, remove and modify norms dynamically. An agent can also flexibly modify the parameters of the system based on whether it is successful in identifying a norm.Norms, Social Norms, Obligations, Norm Identification, Agent-Based Simulation, Simulation of Norms, Artificial Societies, Normative Multi-Agent Systems (NorMAS)

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Tag based model for knowledge sharing in agent society

In this paper we discuss a tag-based model that facilitates knowledge sharing in the context of agents playing the knowledge sharing game. Sharing the knowledge incurs a cost for the sharing agent, and thus non-sharing is the preferred option for selfish agents. Through agent-based simulations we show that knowledge sharing is possible even in the presence of non-sharing agents in the population. We also show that the performance of an agent society can be better when some agents bear the cost of sharing instead of the whole group sharing the cost.Unpublished[1] Holland, J.H.: The Effect of Labels (Tags) on Social interactions. Vol. SFI Working Paper 93-10-064, Santa Fe Institute, Santa Fe, NM (1993) [2] YouTube, www.youtube.com [3] CiteSeer, http://citeseer.ist.psu.edu [4] Riolo, R.L., M.D. Cohen, and R. Axelrod.: Cooperation without Reciprocity. Nature 414, 2001: pp. 441--443 (2001). [5] Hales, D.: Evolving Specialisation, Altruism and Group-Level Optimisation Using Tags. Multi-Agent-Based Simulation II: Third International Workshop, MABS 2002, Bologna, Italy, July 15-16, 2002, Vol. 2581, Lecture notes in computer science, pp. 26--35, Springer Berlin / Heidelberg (2003) [6] Hales, D.: Tag Based Co-operation in Artificial Societies. Ph.D. Thesis, Department of Computer Science, University of Essex, UK, 2001. [7] Folksonomy , http://en.wikipedia.org/wiki/Folksonomy [8] Riolo, R.L.: The Effects of Tag-Mediated Selection of Partners in Evolving Populations Playing the Iterated Prisoner's Dilemma. 1997, Santa Fe Institute. [9] Nowak, M.A. and K. Sigmund.: Evolution of indirect reciprocity by image scoring, Nature vol. 393, pp. 573--577 (1998) [10] Trivers, R: The Evolution of Reciprocal Altruism, Quarterly Review of Biology 46 pp.35-57 (1971) [11] Hamilton, W. D.: The genetical evolution of social behaviour. I, Journal of Theoretical Biology, 1964 Jul; 7(1):1-16. [12] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Altruistic Sharing using Tags”, Proceedings of the 6th International Workshop on Agents and Peer-to-Peer Computing, Estoril, Portugal, May 2008 (in press). [13] Németh, A. and K. Takács.: The Evolution of Proximity Based Altruism, Department of Sociology and Social Policy, 2006, Corvinus University of Budapest, Budapest. [14] Clutten-Brock, T. H., and Parker, G. A.: Punishment in animal societies. Nature 373 (1995), 209 – 216. [15] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Emergence of Sharing Behavior in a Multi-agent Society using Tags”, Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2008), Sydney, Australia, December 2008 (in press)

A collaborative Web-based issue based information system (IBIS) framework

This research focuses on the design and development of an IBIS-based tool called IBISMod, which facilitates a distributed and collaborative decision-making process. IBIS-based systems help analysts and designers in the process of formulating the requirements and design issues associated with complex problems that are difficult to specify. In particular, it captures the rationale behind group decision-making process. The group members are usually distributed over a network and may be working together concurrently. IBISMod is based on Rittel's Issue-Based Information System. This particular implementation is a web-based tool that makes it possible for the participants to work together on a specific problem while they may be physically present in different locations. In order to improve the interactivity, speed and usability of the framework, the AJAX approach has been adopted.UnpublishedConklin, J.(1996). Designing Organizational Memory: Preserving Intellectual Assets in a Knowledge Economy [On line]. Touchstone Consulting Group, Inc. Available from: http://www.touchstone.com/tr/wp/DOM.html. [20 January, 2006]. Conklin, J.(1996). The IBIS Manual: A Short Course in IBIS Methodology. [On line]. Available from: http://cognexus.org/id26.htm. Conklin, J.(2001). Sense-Making and Knowledge Collaboration Tools [On line]. CogNexus Institute. Available from: http://www.cognexus.org/sensemaking.doc. 2006]. Conklin, J. and Begeman, M. l. (1988). "gIBIS: a hypertext tool for exploratory policy discussion." ACM Transactions on Information Systems 6(4): 303-331. Garrett, J. J.(2005). Ajax: A New Approach to Web Applications [On line]. adaptive path. Available from: http://www.adaptivepath.com/publications/essays/archives/000385.php. [20 February, 2006]. Isenmann, S. and Wolf , D. R. (1997). IBIS—a convincing concept…but a lousy instrument? the conference on Designing interactive systems: processes, practices, methods and techniques, Amsterdam, The Netherlands, ACM Press, Karacapilidis, N. and Papadias, D. (1999). "Computer Supported Argumentation and Collaborative Decision Making: The HERMES system." Information Systems 26(4): 259-277. Kunz, W. and Rittel, H. W. J. (1970). Issues as elements of information systems. Working paper. University of California at Berkeley: 1-9. Lee, J. (1990). "SIBYL:A tool for managing t group Decision Rational." CSCW 90 Proc ACM: 79-92. Louridas, P. and Loucopoulos, P. (2000). "A Generic Model for Reflective Design." ACM Transactions on Software Engineering and Methodology 9(2): 199-237. Mackenzie, A., Pidd, M., Rooksby, J., Sommerville, I., Warren, I. and Westcombe, M. (2006). "Wisdom, decision support and paradigms of decision making." European Journal of Operational Research 170(1): 156-171. Mylopoulos, J., Borgida, A., Jarke, M. and Koubarakis (1990). "Telos: Representing knowledge about information systems." ACM Transactions on Information Systems 8: 325-362. Purvis, M. K., Purvis, M. A. and Jones, P. (1996). A Group Collaboration Tool for Software Engineering Projects. International Conference on Software Engineering:Education and Practice, IEEE, 362-369 Ramesh, B. and Dhar, V. (1992). "Supporting Systems Development by capturing Deliberations During Requirements Engineering." IEEE Transactions on Software Engineering 18(6): 498-510. Rittel, H. J. and Webber, M. M. (1984). "Planning problems are wicked problems." development in design methodology: 135-144. Rittel, H. W. J. and Webber, M. M. (1973). "Dilemmas in a General Theory of Planning." Policy Sciences: 155-169. Sun Developer Network.(2005). JDBC Technology [On line]. Available from: http://java.sun.com/products/jdbc/index.jsp. [8 December, 2005]. Veerman, A. L. and Treasure-Jones (1999). Software for problem solving through collaborative. Foundations of argumentative text, Amsterdam, Amsterdam University Press., 203-230 Yakemovic, K. C. B. and Conklin, J. (1990). Observation on a Commercial Use of an Issue-Based Information System. Computer Supported Cooperative Work, Los Angeles, Association for Computing Machinery

A collaborative Web-based issue based information system (IBIS) framework

This research focuses on the design and development of an IBIS-based tool called IBISMod, which facilitates a distributed and collaborative decision-making process. IBIS-based systems help analysts and designers in the process of formulating the requirements and design issues associated with complex problems that are difficult to specify. In particular, it captures the rationale behind group decision-making process. The group members are usually distributed over a network and may be working together concurrently. IBISMod is based on Rittel's Issue-Based Information System. This particular implementation is a web-based tool that makes it possible for the participants to work together on a specific problem while they may be physically present in different locations. In order to improve the interactivity, speed and usability of the framework, the AJAX approach has been adopted.UnpublishedConklin, J.(1996). Designing Organizational Memory: Preserving Intellectual Assets in a Knowledge Economy [On line]. Touchstone Consulting Group, Inc. Available from: http://www.touchstone.com/tr/wp/DOM.html. [20 January, 2006]. Conklin, J.(1996). The IBIS Manual: A Short Course in IBIS Methodology. [On line]. Available from: http://cognexus.org/id26.htm. Conklin, J.(2001). Sense-Making and Knowledge Collaboration Tools [On line]. CogNexus Institute. Available from: http://www.cognexus.org/sensemaking.doc. 2006]. Conklin, J. and Begeman, M. l. (1988). "gIBIS: a hypertext tool for exploratory policy discussion." ACM Transactions on Information Systems 6(4): 303-331. Garrett, J. J.(2005). Ajax: A New Approach to Web Applications [On line]. adaptive path. Available from: http://www.adaptivepath.com/publications/essays/archives/000385.php. [20 February, 2006]. Isenmann, S. and Wolf , D. R. (1997). IBIS—a convincing concept…but a lousy instrument? the conference on Designing interactive systems: processes, practices, methods and techniques, Amsterdam, The Netherlands, ACM Press, Karacapilidis, N. and Papadias, D. (1999). "Computer Supported Argumentation and Collaborative Decision Making: The HERMES system." Information Systems 26(4): 259-277. Kunz, W. and Rittel, H. W. J. (1970). Issues as elements of information systems. Working paper. University of California at Berkeley: 1-9. Lee, J. (1990). "SIBYL:A tool for managing t group Decision Rational." CSCW 90 Proc ACM: 79-92. Louridas, P. and Loucopoulos, P. (2000). "A Generic Model for Reflective Design." ACM Transactions on Software Engineering and Methodology 9(2): 199-237. Mackenzie, A., Pidd, M., Rooksby, J., Sommerville, I., Warren, I. and Westcombe, M. (2006). "Wisdom, decision support and paradigms of decision making." European Journal of Operational Research 170(1): 156-171. Mylopoulos, J., Borgida, A., Jarke, M. and Koubarakis (1990). "Telos: Representing knowledge about information systems." ACM Transactions on Information Systems 8: 325-362. Purvis, M. K., Purvis, M. A. and Jones, P. (1996). A Group Collaboration Tool for Software Engineering Projects. International Conference on Software Engineering:Education and Practice, IEEE, 362-369 Ramesh, B. and Dhar, V. (1992). "Supporting Systems Development by capturing Deliberations During Requirements Engineering." IEEE Transactions on Software Engineering 18(6): 498-510. Rittel, H. J. and Webber, M. M. (1984). "Planning problems are wicked problems." development in design methodology: 135-144. Rittel, H. W. J. and Webber, M. M. (1973). "Dilemmas in a General Theory of Planning." Policy Sciences: 155-169. Sun Developer Network.(2005). JDBC Technology [On line]. Available from: http://java.sun.com/products/jdbc/index.jsp. [8 December, 2005]. Veerman, A. L. and Treasure-Jones (1999). Software for problem solving through collaborative. Foundations of argumentative text, Amsterdam, Amsterdam University Press., 203-230 Yakemovic, K. C. B. and Conklin, J. (1990). Observation on a Commercial Use of an Issue-Based Information System. Computer Supported Cooperative Work, Los Angeles, Association for Computing Machinery

Tag based model for knowledge sharing in agent society

In this paper we discuss a tag-based model that facilitates knowledge sharing in the context of agents playing the knowledge sharing game. Sharing the knowledge incurs a cost for the sharing agent, and thus non-sharing is the preferred option for selfish agents. Through agent-based simulations we show that knowledge sharing is possible even in the presence of non-sharing agents in the population. We also show that the performance of an agent society can be better when some agents bear the cost of sharing instead of the whole group sharing the cost.Unpublished[1] Holland, J.H.: The Effect of Labels (Tags) on Social interactions. Vol. SFI Working Paper 93-10-064, Santa Fe Institute, Santa Fe, NM (1993) [2] YouTube, www.youtube.com [3] CiteSeer, http://citeseer.ist.psu.edu [4] Riolo, R.L., M.D. Cohen, and R. Axelrod.: Cooperation without Reciprocity. Nature 414, 2001: pp. 441--443 (2001). [5] Hales, D.: Evolving Specialisation, Altruism and Group-Level Optimisation Using Tags. Multi-Agent-Based Simulation II: Third International Workshop, MABS 2002, Bologna, Italy, July 15-16, 2002, Vol. 2581, Lecture notes in computer science, pp. 26--35, Springer Berlin / Heidelberg (2003) [6] Hales, D.: Tag Based Co-operation in Artificial Societies. Ph.D. Thesis, Department of Computer Science, University of Essex, UK, 2001. [7] Folksonomy , http://en.wikipedia.org/wiki/Folksonomy [8] Riolo, R.L.: The Effects of Tag-Mediated Selection of Partners in Evolving Populations Playing the Iterated Prisoner's Dilemma. 1997, Santa Fe Institute. [9] Nowak, M.A. and K. Sigmund.: Evolution of indirect reciprocity by image scoring, Nature vol. 393, pp. 573--577 (1998) [10] Trivers, R: The Evolution of Reciprocal Altruism, Quarterly Review of Biology 46 pp.35-57 (1971) [11] Hamilton, W. D.: The genetical evolution of social behaviour. I, Journal of Theoretical Biology, 1964 Jul; 7(1):1-16. [12] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Altruistic Sharing using Tags”, Proceedings of the 6th International Workshop on Agents and Peer-to-Peer Computing, Estoril, Portugal, May 2008 (in press). [13] Németh, A. and K. Takács.: The Evolution of Proximity Based Altruism, Department of Sociology and Social Policy, 2006, Corvinus University of Budapest, Budapest. [14] Clutten-Brock, T. H., and Parker, G. A.: Punishment in animal societies. Nature 373 (1995), 209 – 216. [15] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Emergence of Sharing Behavior in a Multi-agent Society using Tags”, Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2008), Sydney, Australia, December 2008 (in press)

A framework for distributed workflow systems

Workflow management systems (WFMS) are being adopted to assist the automation of business processes that involve the exchange of information. As a result of developments in distributed information system technology, it is now possible to extend the WFMS idea to wider spheres of activity in the industrial and commercial world and thereby to encompass the increasingly sprawling nature of modern organisations. This paper describes a framework under development that employs such technology so that software tools and processes may interoperate in a distributed and dynamic environment. The framework employs Petri nets to model the interaction between various sub-processes. CORBA technology is used to enable different participants who are physically disparate to monitor activity in and make resource-level adaptations to their particular subnet.Unpublished[1] R. Bastide, D. Buchs, M. Buffo, F. Kordon and O. Sy, Questionnaire for a taxonomy of Petri net dialects [2] S. Christensen and N. Damgaard Hansen, “Coloured Petri nets extended with channels for synchronous communication”, LNCS 815, Application and Theory of Petri Nets 1994, Proc of 15th International Conference, Zaragoza, Spain, June 1994, R. Valette (ed.), pp. 159-178, Springer-Verlag, Berlin, 1994 [3] C.A. Ellis, K. Keddara and G. Rozenberg, “Dynamic change within workflow systems”, Proc of Conference on Organizational Computing Systems (COOCS’95), Milpitas, CA, August 1995, pp. 10-21, ACM Press, New York, 1995 [4] E. Gamma, JHotDraw, 1998 [5] M. Hammer and J. Champy, Reengineering the Corporation, Harper Business, New York, 1993 [6] Y. Han, A. Sheth and C. Bussler, “A taxonomy of adaptive workflow management”, 1998 ACM Conference on Computer Supported Cooperative Work (CSCW-98), Seattle, WA, November 1998 [7] K. Jensen, Coloured Petri Nets – Basic Concepts, Analysis Methods and Practical Use, Vol. 1: Basic Concepts, Springer-Verlag, Berlin, 1992 [8] O. Kummer and F. Wienberg, Renew – User Guide, Release 1.3 University of Hamburg, Department for Informatics, Theoretical Foundations Group and Distributed Systems Group, September 2000 [9] O. Kummer, “Simulating synchronous channels and net instances”, 5. Workshop Algorithmen und Werkzeuge für Petrinetze, J. Desel, P. Kemper, E. Kindler and A. Oberweis (eds.), pp. 73-78, Forschungsbericht Nr. 694, Universität Dortmund, Fachbereich Informatik, October 1998 [10] O. Kummer. “Tight integration of Java and Petri nets”, 6. Workshop Algorithmen und Werkzeuge für Petrinetze, J. Desel and A. Oberweis (eds.), pp. 30-35, J.W. Goethe-Universität, Institut für Wirtschaft-informatik, Frankfurt am Maim, October 1999 [11] P.D. O’Brien and W.E. Wiegand, “Agent based process management: applying intelligent agents to workflow”, The Knowledge Engineering Review, 13(2):1-14, June 1998 [12] S.W. Sadiq, O. Marjanovic and M.E. Orlowska, “Managing change and time in dynamic workflow processes”, International Journal of Cooperative Information Systems, 9(1-2):93-116, World Scientific Publishing Company, 2000 [13] W.M.P. van der Aalst, “The application of Petri nets to workflow management”, The Journal of Circuits, Systems and Computer, 8(1):21-66, 1998 [14] W.M.P. van der Aalst, “Three good reasons for using a Petri-net-based workflow management system”, Proc of International Working Conference on Information and Process Integration in Enterprises (IPIC’96), S. Navathe and T. Wakayama (eds.), pp. 179-201, Camebridge, Massachusetts, November 1996 [15] Workflow Management Coalitition, The Workflow Reference Model, Document No. TC00-1003, Issue 1.1, 199

Partner selection mechanisms for agent cooperation

In a multi-agent system, a single agent may not be capable of completing complex tasks. Therefore agents are required to form a team to fulfill the task requirements. In this paper an agent model is introduced that facilitates cooperation among agents. A multi-threaded multi-agent simulation framework is designed to test the model. The experimental results demonstrate that the model is significantly useful in achieving cooperation under various environmental constraints. It also allows agents to adjust their teammate selection strategies according to environmental constraints.Unpublished[1] Tesser, A. and Shaffer, D., Attitudes and Attitude Change. Annual Review of Psychology, 1990. 41(1): p. 479-523. [2] Airiau, P., Sen, S. and Dasgupta, P., Effect of joining decisions on peer clusters, in Proceedings of the fifth international joint conference on Autonomous agents and Multiagent systems. 2006, ACM: Hakodate, Japan. p. 609-615. [3] Dutta, P.S., Moreau, L. and Jennings, N.R. Finding interaction partners using cognition-based decision strategies. in Proceedings of the IJCAI-2003 workshop on Cognitive Modeling of Agents and Multi-Agent Interactions. 2003. [4] Sen, S., Gursel, A. and Airiau, S. Learning to identify beneficial partners. in the Proceedings of the Workshop on Adaptive and Learning Agents at the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 2007. USA. [5] Ahn, J., DeAngelis, D. and Barber, S. Attitude Driven Team Formation using Multi-Dimensional Trust. in IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07). 2007. [6] Dutta, P.S. and Sen, S., Forming stable partnerships. Cognitive Systems Research, 2003. 4(3): p. 211-221. [7] Banaei-Kashani, F. and Shahabi, C. Criticality-based analysis and design of unstructured peer-to-peer networks as" Complex systems. in Cluster Computing and the Grid, 2003. Proceedings. CCGrid 2003. 3rd IEEE/ACM International Symposium on. 2003