Engineering Conscious Machines

1. Engineering Conscious Machines Ricardo Sanz Autonomous Systems Laboratory Universidad Politécnica de Madrid Models of ConsciousnessESF/PESC Exploratory Workshop BirminghamSeptember 1-3, 2003

2. Abstract • There are several ongoing attempts to build conscious machines using different types of technologies and engineering methods: conventional symbolic AI, neural networks, non-linear dynamical systems, etc. • If a computer-based mind for a machine is going to be made as conscious as humans, it is quite clear that it will be a complex software/hardware application. • There are several approaches to engineering complex software systems and they will be reviewed in this talk with a particular emphasis on constructive methods (obviously due to a bias of the speaker). Emergent methods will be also analysed and convergent technology introduced. • We will analyse the possibilities of the different methods an try to extrapolate from present software engineering technology the degree of effort needed, the time scope and the tradeoffs of different designs and implementation technologies.

3. A Clear Need (of machine consciousness)

4. Simple feedback Sensors Actuators Physical Machine

5. Enhanced feedback Goals Filter Executor Sensor Actuator Physical Machine

6. Stateless vs Stateful Goals Executor State Filter Sensor Actuator Physical Machine

7. Layering Goals Layer 2 Filter Executor State Layer 1 Executor State Filter Sensor Actuator Physical Machine

8. Deliverative/Reactive ReasoningEngine Knowledge State Filter Executor State Filter Sensor Actuator Physical Machine

9. Model-based control WorldModel Modeller Executor Executor State Filter Sensor Actuator Physical Machine

10. Introspection and Reflection QueryEngine Meta-level representation ReasoningEngine Knowledge State Filter Sensor Actuator Physical Machine

11. Hierarchy and heterarchy Controller Controller Controller Controller Controller Controller Controller Controller Controller Controller Controller Controller Controller Physical Machine

12. Multiresolutional reflective control • Revonsuo: “multiple levels of organisation, forming a hierarchical, causal mechanical network” • Industrial controller evolution is mimicking biological mind evolution • Now, we’re in the phase of creating conscious controllers (even when most control engineers don’t know)

13. Autonomic Systems (as IBM sees them) • Adapts to changes in its environment • Strives to improve its performance • Heals when it is damaged • Defends itself against attackers • Exchanges resources with unfamiliar systems • Communicates through open standards • Anticipates users’ actions • Possesses a sense of self SciAm May 06, 2002

14. Positioning • Need of Artificial Consciousness • A foreigner point of view • A customer point of view • A reductionist point of view • In summary: an engineer point of view

15. Structure of the talk • Give a personal perspective on present state of affairs • Give an personal assessment on engineering processes for AC • Summarize a personal, half-baked, engineering vision on UTC

16. State of Affairs A Personal Perspective

17. Some Views • Taylor: “Attention is the gateway to consciousness” • Salichs: “Basic consciousness: Current (here and now) self” • Holland & Goodman: “Consciousness via incremental intelligence” • Sloman & Chrisley: “Architectures that support mental processes conected with normal notion of consciousness” • Cleermans: “flexible, adaptive control over behavior”

18. CODAM • “The corollary discharge of attention module (CODAM) carries the signal of ownership of the about-to-be-experienced contentful activity arriving later on the sensory input buffer from sensory feedback.” J.G. Taylor, From Matter To Mind • Translation into conventional control tech: • CODAM fills Measure’s“Owner” slot with “I” Sensor Measure

19. CODAM • Splitting of attention control signal: • to focus sensorial attention and • to create the PRS waiting for sensor inputs • CODAM is a simple sensor control architecture • It seems complex due to all that confusing decorations based on neurophysiological details about functional topology of human brains • CODAM generates a “self”, i.e. a continuously refreshed dynamical model of the agent

20. CogAff • Consciousness is not just a bag of information processing functions. It is a core architectural principle of minds (with a demonstrated evolutionary advantage). • There is a difference between an architectural schema like CogAff (a pattern) and an agent architecture like H-CogAff (an instance) • There exist reusable mind design patterns

21. First Assessment • We share a single core engineering model of consciousness (while unconsciously) • Neuroscientific/psychological data corroborates this model • Varying visions are just views of this core model coloured of personal backgrounds

22. The modeling machine • Evolution has engineered a model-based learning controller: the Central Nervous System • This machine generatesdifferent types of models to act in the world

23. Naïve models of reality • Judging that an animal will not mind being killed if it is not offended, Eskimos take various ritual precautions before, during, and after the hunt. • The rationale (the behavioral model of the world+agent) lies in the belief that animal spirits exist independent of bodies and are reborn: an offended animal will later lead his companions away so that the hunter may starve. • Just projections of what is best known: the self

24. Survivor Models • Elementary, ad-hoc, experience-based causal models of reality • Examples: agriculture, mating, micronesian navigation(rowing to move the islands to certain positions in thehorizon) • Cleermans: “representational systems that can be adaptively modified by ongoing experience”

25. Deep models • Behavior based on deep models outperforms behaviour based on behaviourally learnt models • Scientific theories of reality

26. The machine of the world • Science and technology have established themselves as the best models and tools to control the machinery of the world

27. Engineering Processes How can we build these systems ?

28. Processes • Alternative processes: • Theory-based construction (Sloman) • Incremental hacking (Holland) • Reverse engineering brains (Redgrave) • Learning from raw stuff (Aleksander) • Emergent/autopoietic growth (Doran/Ziemke) • What’s contingent and what’s necessary? • What’s the core design pattern of consciousness ?

29. Beyond “Normal” Agents • Dependable control agents do have requirements well beyond what is considered “normal” intelligent function: • Real-time performance • Embeddability • High-assurance • Evolvability / Ugradeability

30. Design Artefact Design-based processes • Chrisley: ”Flow of effect is not one-way” • Holland: ”toy around and keep eyes open” Theory

31. Complex Systems Engineering • Doran: “Our ability to design and build complex agent architectures is limited” • Round-trip engineering is not possible due to sybsystems interaction explosion • Learning / adaptation methods are useless due to design-space complexity explosion • Architecture-based product families and modularity can help a little • Design for emergence is a promising alternative

32. The Way • Learn from evolution:From model-based behavior tomodel-based engineering • Theory-based, model-implemented, tool-supported engineering processes for mind construction • Plenty of examples out there

33. Personal Vision What else ?

34. Man on His World World Man

35. Meaning generation • Autonomous systems: • Generate meanings from data (typically from sensory inputs) • Use their continuosly updated mental models to control behavior • Meanings are equivalence classes of agent+world trajectories in state-space in relation with agent’s value system (projections into the future including counter-factuals) • Hesslow: “simulated perception can be elicited by (simulated) behaviour”

36. supercritical protection thresold critical now future subcritical Example 1 • Consider: • a nuclear reactor • the primary control system • the primary protection system • What’s the meaning of a particluar measure of neutronic flow ? • The’re two different meanings • for the control system • for the protection system

37. Example 2 • Consider that you’re Owen driving along Bristol Road • Consider the meaning of a road sign saying “Manor House to the right” • If Owen becomes aware of the sign, the value of his future along Bristol Road changes

38. Awareness and Consciousness • A system is aware if it is generating meanings from perceptions (including inner perceptions) • A system is conscious if “I am aware” is valid in the the present state of affairs (it is generated from the perceptual flow, i.e. the system is aware of itself). • Lacombe: “It is impossible to separate awareness, consciousness and understanding”.

39. Strong points of this model • Unifying • Machine applicable • Explains other related phenomena: e.g. attention • Of what do you calculate potential effects when resources are scarce? • Only of those pieces that most assuredly can affect your future: focus of attention • Holland: “simulate only the part of the world that can mostly affect the agent”

40. Another Strong Point • This model provides a metric • It is possible to calculate the degree of coverage of future trajectories • It makes possible the comparison of awareness levels of systems in the same conditions (i.e. experiencing the same sensor space, including inner space)

41. Synthesising (Control) State • A model-based, sensor processing + control hierarchy reduces data (by abstraction+integration) until reaching a single, unique, compact representation of the self • This self-generation process is mostly hidden and hence the self seems directly perceivable and inmaterial

42. Synthesising (Control) State • Manzotti: “uniqueness is important for consciousness” • Haikonen: “inmaterial mind … missing perception of material carrying symbols or processes” • Anceau: “conscious behaviour is sequential”

43. Summary • Mind is a multiresolutional phenomenon of control • Minds generate and use dynamicmodels • At any resolution level, meaning generation generates awareness • At any resolution level, mind reflection generates consciosuness • We -usually- only can talk about the upper level

44. Thanks for being conscious during my talk! Questions ?