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AGI−19 Shenzhen, China

AGI−19 Shenzhen, China. Cognitive Model of Brain-Machine Integration Zhongzhi Shi Zeqin Huang Institute of Computing Technology Chinese Academy of Sciences h ttp:// www.intsci.ac.cn/en/shizz. Contents Outline. Introduction. Cognitive Model. Environment Awareness.

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AGI−19 Shenzhen, China

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  1. AGI−19Shenzhen, China Cognitive Model of Brain-Machine Integration Zhongzhi Shi Zeqin Huang Institute of Computing Technology Chinese Academy of Sciences http://www.intsci.ac.cn/en/shizz Zhongzhi Shi: Brain Machine Integration

  2. Contents Outline Introduction Cognitive Model Environment Awareness Joint Intention Based Collaboration Conclusions and Future Works Zhongzhi Shi: Brain Machine Integration

  3. Brain Machine Interface 1999 Nat Neurosci 2012 Nature 2000 Nature 2002 Science 2002 nature 2011 Nature 2008 Nat Neurosci/ Nature 2004 Science 2006 Nature 鼠 猴 人 Zhongzhi Shi: Brain Machine Integration

  4. Brain Machine Interface Zhongzhi Shi: Brain Machine Integration

  5. Brain Machine I3 Bidirectional Interaction • Integration • Interaction Decode • Interface Encode Zhongzhi Shi: Brain Machine Integration

  6. Musk Neuralink image credit | Bloomberg • On March 28, 2017, SpaceX and Tesla CEO Elon Musk is backing a brain-computer interface venture called NeuralinkCorp ，a company devoted to developing neural implants.It isa closer merger of biological intelligence and digital intelligence Zhongzhi Shi: Brain Machine Integration

  7. Musk Neuralink image credit | Bloomberg • On Tuesday evening, July 16, 2019, thousands of supporters and opponents of Elon Musk watched Neuralink's first live Internet show. Zhongzhi Shi: Brain Machine Integration

  8. Computing Theory& Method for Perception & Cognition of Brain Machine Integration 1. Brain information representation, encode and decode 2. Cognitive computing for brain-machine collaboration Scientific issue3 Scientific issue2 Scientific issue1 4. Experimental platforms and Application verification 3. Mutual adaptation and motor functional reconstruction Zhongzhi Shi: Brain Machine Integration

  9. Cognitive Computing Model for BMI - Computing Model - Environment Awareness - Brain Machine Collaboration Zhongzhi Shi: Brain Machine Integration

  10. Contents Outline Introduction Cognitive Model Environment Awareness Joint Intention Based Collaboration Conclusions and Future Works Zhongzhi Shi: Brain Machine Integration

  11. Mind Model CAM

  12. ABGP Model

  13. Cognitive Model of Brain Machine Integration CAM model ABGP model Zhongzhi Shi: Brain Machine Integration

  14. Contents Outline Introduction Cognitive Model Environment Awareness Joint Intention Based Collaboration Conclusions and Future Works Zhongzhi Shi: Brain Machine Integration

  15. Environment Awareness • Cyborg intelligent systems require bidirectional information perception between rat brain and computer. Awareness is the state or ability to perceive, to feel events, objects or sensory patterns, and cognitive reaction to a condition or event. Awareness has four basic characteristics: • Awareness is knowledge about the state of a particular environment. • Environments change over time, so awareness must be kept up to date. • Agents maintain their awareness by interacting with the environment. • Awareness establishes usually an event. Zhongzhi Shi: Brain Machine Integration

  16. Visual Imagery Processing • Framework Cite from:S. M. Kosslyn. MENTAL IMAGES AND THE BRAIN. COGNITIVE NEUROPSYCHOLOGY, 2005, 22 (3/4), 333–347 Zhongzhi Shi: Brain Machine Integration

  17. Environment Awareness The brain machine collaborative awareness model is defined as 2-tuples: {Element, Relation}, where Element of awareness is described as follows: a) Who: describes the existence of agent and identity the role, answer question who is participating? b) What: shows agent’s actions and abilities, answer question what are they doing? And what can they do? Also can show intentions to answer question what are they going to do? c) Where: indicates the location of agents, answer question where are they? d) When: shows the time point of agent behavior, answer question when can action execute? Zhongzhi Shi: Brain Machine Integration

  18. Basic Relationships • Task relationships define task decomposition and composition relationships. Task involves activities with a clear and unique role attribute • Role relationships describe the role relationship of agents in the multi-agent activities. • Operation relationships describe the operation set of agent. • Activity relationships describe activity of the role at a time. • Cooperation relationships describe the interactions between agents. Zhongzhi Shi: Brain Machine Integration

  19. CNN Model • Weaknesses  • Weak capability to overcome some noise • Convolutional Neural Networks (CNN) • Biology visual theory • Multi-level hierarchy feature representation Zhongzhi Shi: Brain Machine Integration

  20. Generative Stochastic Networks Bengio Y, Éric T, Alain G, et al. Deep Generative Stochastic Networks Trainable by Backprop[J]. Computer Science, 2013, 2:226–234. • Generative Stochastic Networks (GSN) • Probability model • Without explicitly specifying a probabilistic graphical model • Learning deep generative model through back-propagation • Stronger capability to overcome noise • Weaknesses  • Weak capability to extract the multi-level hierarchies of invariant features Zhongzhi Shi: Brain Machine Integration

  21. CGSM Model • Convolutional Generative Stochastic Model(CGSM) • Multi-level hierarchy feature representation • Stronger capability to overcome noise Zhongzhi Shi: Brain Machine Integration

  22. Roadmap Data Random Noise NoNoise Zhongzhi Shi: Brain Machine Integration

  23. No Noise Zhongzhi Shi: Brain Machine Integration

  24. With Random Noise Zhongzhi Shi: Brain Machine Integration

  25. Contents Outline Introduction Cognitive Model Environment Awareness Joint Intention Based Collaboration Conclusions and Future Works Zhongzhi Shi: Brain Machine Integration

  26. Joint Intention • In the joint-intention theory, a team is defined as “a set of agents having a shared objective and a shared mental state.” • Agent joint intention means an agent wants to achieve a formula, which corresponds to the agent’s goal. • Ajoint intention to perform a particular action is a joint commitment to enter a future state wherein the agents mutually believe the collaborative action is imminent just before they perform it Zhongzhi Shi: Brain Machine Integration

  27. Individual Intentions • 1984 Bratman, BDI • 1990 Cohen and Levesque, intention model. • 1990 Pollack, intention model • 1988/1989 Werner, intention model, • Social roleRrol = <Irol, Srol, Vrol> Zhongzhi Shi: Brain Machine Integration

  28. Joint Intentions • 1989 Conte, Group Mind • 1990 Searle, collective intentions • 1990 Grosz and Sidner, Shared Plan • 1988 Tuomela and Miller, we-intentions • 1990 Rao et al. Social Plans • 1990 Singh Group Intentions Zhongzhi Shi: Brain Machine Integration

  29. Joint Intentions 1992 Jennings claimed the need to describe collectives as well as individuals. • agents must agree on a common goal. • agents must agree they wish to collaborate to achieve their shared objective. • agents must agree a common means of reaching their objective. • action inter-dependencies exist and must be catered for in general terms. Zhongzhi Shi: Brain Machine Integration

  30. GRATE* : A Cooperation Knowledge Level System Nicholas Robert Jennings. Joint Intentions as a Model of Multi-Agent Cooperationin Complex Dynamic Environments. University of London, 1992. Zhongzhi Shi: Brain Machine Integration

  31. Joint Intention Zhongzhi Shi: Brain Machine Integration

  32. Description Logic Description Logic • Concepts and Role • Tbox——Assertions • Abox——Instance • Reasoning mechanism in terms of Tbox and Abox Zhongzhi Shi: Brain Machine Integration

  33. Description Logic K B TBox(Scheme) Man = Human ⊓ Male Happy-father = Human ⊓∃ Has-child.Female⊓… Abox(Data) John: Happy-father <John,Mary> : Has-child Reasoning Interface Zhongzhi Shi: Brain Machine Integration

  34. Dynamic Description Logic • Concept name：C1, C2, …； • Role name：R1, R2, …； • Individual constant：a, b, c, …； • Individual variable：x, y, z, …； • Concept operation：, ⊓, ⊔, , ； • Axiom operation：, ∧, ； • Action：A1, A2, …； • Action construction：；(composition)，⋃ (alternation)，* (repeat)，？(test)； • Action variable：α,β, …； • Axiom variable：, , π, …； • State variable：u, v, w, …； Zhongzhi Shi: Brain Machine Integration

  35. Dynamic Description Logic Concepts in DDL are defined as the following: • (1) Primitive concept P, top ⊤ and bottom ⊥ are concepts. • (2) C, C⊓D, C⊔D are concepts. • (3) ∃R.C, ∀R.C are concepts. Zhongzhi Shi: Brain Machine Integration

  36. Dynamic Description Logic An action description is the form of where (1) A is the action name. (2) x1, …, xnare individual variables, which denote the objects the action operate on. (3) PAis the set of preconditions, which must be satisfied before the action is executed. (4) EAis the set of results, which denote the effects of the action. Zhongzhi Shi: Brain Machine Integration

  37. Distributed Dynamic Description Logic Bridge rules provide an important mechanism describing semantic mapping and propagating knowledge for distributed dynamic description logics(D3L). The current research focuses on the homogeneous bridge rules which only contain atomic elements. Xiaofei Zhao, Dongping Tian, Limin Chen, Zhongzhi. Reasoning Theory for D3L with Compositional Bridge Rules. IFIP IIP 2012, 2012, 106-115. Zhongzhi Shi: Brain Machine Integration

  38. Collaborative Decision Making • Collaborations occur over time as organizations interact formally and informally through repetitive sequences of negotiation, and commitment development and execution. Under the support of the National Program on Key Basic Research Project (973) we focus on Computational Cognitive Models for Brain–Machine Collaborations: • Awareness-Based Collaboration • Motivation-Based Collaboration • Joint Intention-Based Collaboration • Zhongzhi Shi, Jianhua Zhang, Xi Yang, Gang Ma, Baoyuan Qi, JinpengYue. Computational Cognitive Models for Brain-Machine Collaborations. IEEE Intelligent Systems 29(6): 24-31 (2014). Zhongzhi Shi: Brain Machine Integration

  39. Maze Simulation of Rat Cyborg Zhongzhi Shi: Brain Machine Integration

  40. Rat Cyborg In the automatic navigation of rats, five bipolar stimulating electrodes separately are invasive implanted in medial forebrain bundle (MFB), somatosensory cortices (SI), and periaqueductal gray matter (PAG) of the rat brain. There is also a backpack fixed on the rat to receive the wireless commands. Zhongzhi Shi: Brain Machine Integration

  41. Rat Cyborg The rat cyborg was explored the neural mechanisms underlying multimodal sensory associations by how the large-scale sensorimotor network integrates multimodal sensory information and further generates precise motor-control output. Zhongzhi Shi: Brain Machine Integration

  42. Contents Outline Introduction Cognitive Model Environment Awareness Joint Intention Based Collaboration Conclusions and Future Works Zhongzhi Shi: Brain Machine Integration

  43. Conclusions • Proposed a cognitive model of brain • machine integration • Environment awareness • Joint intention for collaboration Zhongzhi Shi: Brain Machine Integration

  44. Thank You Question! Intelligence Science http://www.intsci.ac.cn/ Zhongzhi Shi: Brain Machine Integration

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