1. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 1 Mind out of Programmable Matter : Exploring Unified Models of Emergent System Autonomy Prof. A. Taleb-Bendiab Liverpool John Moores University a.talebbendiab@livjm.ac.uka.talebbendiab@livjm.ac.uk, a.talebbendiab@livjm.ac.uk www.cms.livjm.ac.uk/talebwww.cms.livjm.ac.uk/taleb, www.cms.livjm.ac.uk/2nrich www.cms.livjm.ac.uk/taleb

2. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 2 Outline Radical Agent Concepts? or Radical Agent Concepts? or –Achieving DAI ambition –Brief overview How to get there? How to get there? –a unifying model What’s our focus What’s our focus –Autonomic systems engineering Conclusions Conclusions

3. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 3 The State of AI Research – Marvin Minsky (circa 2003) “… Unfortunately, the strategies most popular among AI researchers in the 1980s have come to a dead end, Minsky said. So-called "expert systems," which emulated human expertise within tightly defined subject areas like law and medicine, could match users' queries to relevant diagnoses, papers and abstracts, yet they could not learn concepts that most children know by the time they are 3 years old.” http://www.wired.com/news/technology/0,1282,58714,00.html -- Though – Stuart Russell said “… researchers who study learning, vision, robotics and reasoning have made tremendous progress.” -- Tom Mitchell “…The question is, what is the best research strategy to get (us) from where we are today to an integrated, autonomous intelligent agent?"

4. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 4 The Problems Accumulating Common Sense Accumulating Common Sense –Propositional Account of Knowledge –Acquiring Knowledge What is the best method for representing knowledge and common sense? What is the best method for representing knowledge and common sense? How can reasoning and analysis of knowledge be achieved? How can reasoning and analysis of knowledge be achieved? How can differing approaches be integrated into an overall model? How can differing approaches be integrated into an overall model? Reasoning on Propositions Reasoning on Propositions –Agent model –Representational Formalism Diverse Conflicting Approaches Diverse Conflicting Approaches –No unifying model Programming, control and/or Interaction Programming, control and/or Interaction

5. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 5 Reasoning Systems Agent Models Agent Models –BDI, BOID etc.. Specified by differing formalisms. Specified by differing formalisms. Application/Domain specific. Application/Domain specific. Little support for multi-agent cooperative systems, swarms, self-organisation, etc. Little support for multi-agent cooperative systems, swarms, self-organisation, etc. Representational Formalism Representational Formalism –A huge variety of formalisms are available Process algebras, Logical calculi, etc. Process algebras, Logical calculi, etc. Conflicting Approaches to Modelling Conflicting Approaches to Modelling –Top down, Centralised System Controller System Controller Policy and/or norm-based systems Policy and/or norm-based systems –Bottom up, distributed control Swarming Systems Swarming Systems Self-organisation Self-organisation

6. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 6 Radical Rethinking? #1 mechanisms mechanisms –knowledge, deliberation, action and control interact to form intelligent autonomous agents, be they deliberative, intentional or deliberative, intentional or purely reactive particles/actors. purely reactive particles/actors. –Via logical formal modelling techniques a unified model combining “2 nd order” multi- agency a unified model combining “2 nd order” multi- agency –Meta-layering Systems of systems Systems of systems Observer model for adjustable self-regulation Observer model for adjustable self-regulation

7. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 7 Our Approach #1 How do we do it? How do we do it? –Formal semantics of model-based and emergent regulatory structure of autonomic self-regenerative systems. –A (stochastic) situation calculus for unifying formalism to spec. collective and individual behaviour with coordination model (see vertical vs horizontal layers). collective and individual behaviour with coordination model (see vertical vs horizontal layers). Observer component monitors behaviour: Observer component monitors behaviour: Top down model for observer Top down model for observer Bottom up model for system agents Bottom up model for system agents

8. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 8 A Hybrid Model: Viable Intelligent Agent Architecture

9. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 9 An Adjustable Deliberation Deliberative frameworks: Deliberative frameworks: –Beliefs-Desires-Intentions (BDI) Bratman et al. Beliefs: representing the current state of its world Beliefs: representing the current state of its world desires representing the agent’s ideal world. desires representing the agent’s ideal world. –Mismatch, between B & D triggers the intentions to rectify the current state to the ideal state. To include normative behaviour and thus cooperation and coordination in multi-agent systems To include normative behaviour and thus cooperation and coordination in multi-agent systems –Belief-Obligation-Intention-Desire model (BOID) –Epistemic-Deontic-Axiologic (EDA) model –Extended BDI

10. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 10 Enacting Adjustable Autonomic Models #1 We are concerned with how to enact SC formulation and its deployment We are concerned with how to enact SC formulation and its deployment –Many approaches including Handcrafted code, automated code gen., etc. Handcrafted code, automated code gen., etc. –We are developing a declarative meta- language –JBel based on C#

11. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 11 Example: Grid-Based Decision Systems

12. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 12 Work in Progress #1 A Logical Treatment of Control Emergence in Complex Self-Organising Systems A Logical Treatment of Control Emergence in Complex Self-Organising Systems –Case-study: Swarm Architecture (see ANTS) Defined a set of imperatives eg: Defined a set of imperatives eg: Imperative I Imperative I –When a worker (w) is registered on the mission, by registering with the Ruler team (T), and has not entered any failure state leading to its “death”, the Rulers possess a team commitment to connect with the worker, should it ever be disconnected. Imperative 2 Imperative 2 –The ruler team must have be made up of at least a specified number of ruler units

13. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 13 Work in Progress #2 A number of logically provable consequences emerge as a result of imperative I. eg: A number of logically provable consequences emerge as a result of imperative I. eg: When a worker registers or unregisters with a ruler the ruler has a commitment to make this fact common knowledge (mutually believed) by the ruler team. When a worker registers or unregisters with a ruler the ruler has a commitment to make this fact common knowledge (mutually believed) by the ruler team. If a ruler individually comes to believe that a registered worker is not connected then it has a commitment to make this common knowledge. If a ruler individually comes to believe that a registered worker is not connected then it has a commitment to make this common knowledge. Etc. Etc.

14. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 14 Conclusions and Further works Emergence Emergence –Logical Consequences Minimal rule set Minimal rule set –Evolutionary Monitoring Arises from incompleteness of logic Arises from incompleteness of logic Many techniques may be used to assess new behaviour Many techniques may be used to assess new behaviour Software Learning Software Learning –Knowledge, common sense, experience bounded Autonomy and governance bounded Autonomy and governance

15. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 15Acknowledgements Acknowledgements – –The researchers and staff: Martin Randles, Phil Miseldine, Andy Laws, etc. www.cms.livjm.ac.uk/2nrich www.cms.livjm.ac.uk/cloud www.cms.livjm.ac.uk/taleb – –Sponsors and Partners EPSRC Christies and Linda McCartney NHS trusts

16. Prof. A. Taleb-Bendiab, Talk: WRAC’05, Paper: MR et al, Washington, Date: 20/09/2005, Page: 16 That’s the end – so I’m off !