Cognitive Systems: A Framework for Intelligent Behavior

1. A Cognitive Architecture for Physical Agents Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California USA http://cll.stanford.edu/ Thanks to D. Choi, K. Cummings, N. Nejati, S. Rogers, S. Sage, and D. Shapiro for their contributions. This talk reports research. funded by grants from DARPA IPTO and the National Science Foundation, which are not responsible for its contents.

2. Cognitive Systems The original goal of artificial intelligence was to design and implement computational artifacts that: handled difficult tasks that require cognitive processing; combined many capabilities into integrated systems; provided insights into the nature of mind and intelligence. Instead, modern AI has divided into many subfields that care little about cognition, systems, or intelligence. But the challenge remains and we need far more research on cognitive systems.

3. language planning perception reasoning action learning The Fragmentation of AI Research 

4. The Domain of In-City Driving • Consider driving a vehicle in a city, which requires: • selecting routes • obeying traffic lights • avoiding collisions • being polite to others • finding addresses • staying in the lane • parking safely • stopping for pedestrians • following other vehicles • delivering packages These tasks range from low-level execution to high-level reasoning.

5. In 1973, Allen Newell argued “You can’t play twenty questions with nature and win”. Instead, he proposed that we: Newell’s Critique move beyond isolated phenomena and capabilities to develop complete models of intelligent behavior; demonstrate our systems’ intelligence on the same range of domains and tasks as humans can handle; view artificial intelligence and cognitive psychology as close allies with distinct but related goals; evaluate these systems in terms of generality and flexibility rather than success on a single class of tasks. However, there are different paths toward achieving such systems.

6. language planning perception reasoning action learning A System with Communicating Modules software engineering / multi-agent systems

7. language planning perception reasoning action learning A System with Shared Short-Term Memory short-term beliefs and goals blackboard architectures

8. Integration vs. Unification Newell’s vision for research on theories of intelligence was that: cognitive systems should make strong theoretical assumptions about the nature of the mind; theories of intelligence should change only gradually, as new structures or processes are determined necessary; later design choices should be constrained heavily by earlier ones, not made independently. A successful framework is all about mutual constraints, and it should provide a unified theory of intelligent behavior. He associated these aims with the idea of a cognitive architecture.

9. language planning perception reasoning action learning A System with Shared Long-Term Memory short-term beliefs and goals long-term memory structures cognitive architectures

10. language planning perception reasoning action learning A Constrained Cognitive Architecture short-term beliefs and goals long-term memory structures

11. In this talk I will use one such framework  ICARUS to illustrate the advantages of cognitive architectures. ICARUS incorporates a variety of assumptions from psychological theories; the most basic are that: The ICARUS Architecture Short-term memories are distinct from long-term stores Memories contain modular elements cast as list structures Long-term structures are accessed through pattern matching Cognition occurs in retrieval/selection/action cycles Performance and learning compose elements in memory These claims give ICARUS much in common with other cognitive architectures like ACT-R, Soar, and Prodigy.

12. Architectural Commitment to Memories • For each memory, a cognitive architecture also commits to: • the encoding of contents in that memory; • the organization of structures within the memory; • the connections among structures across memories. A cognitive architecture makes a specific commitment to: long-term memories that store knowledge and procedures; short-term memories that store beliefs and goals; sensori-motor memories that hold percepts and actions. Each memory holds different content the agent uses in activities.

13. Cognitive psychology makes important representational claims: Ideas about Representation • concepts and skills encode different aspects of knowledge that are stored as distinct cognitive structures; • cognition occurs in a physical context, with concepts and skills being grounded in perception and action; • many mental structures are relational in nature, in that they describe connections or interactions among objects; • long-term memories have hierarchical organizations that define complex structures in terms of simpler ones; • each element in a short-term memory is an active version of some structure in long-term memory. ICARUS adopts these assumptions about the contents of memory.

14. ICARUS’ Memories Perceptual Buffer Short-Term Belief Memory Long-Term Conceptual Memory Environment Long-Term Skill Memory Short-Term Goal Memory Motor Buffer

15. ICARUS encodes two forms of general long-term knowledge: Representing Long-Term Structures Conceptual clauses: A set of relational inference rules with perceived objects or defined concepts in their antecedents; Skill clauses: A set of executable skills that specify: a head that indicates a goal the skill achieves; a single (typically defined) precondition; a set of ordered subgoals or actions for achieving the goal. These define a specialized class of hierarchical task networks in which each task corresponds to a goal concept. ICARUS’ syntax is very similar to Nau et al.’s SHOP2 formalism for hierarchical task networks.

16. ICARUS Concepts for In-City Driving ((in-rightmost-lane ?self ?clane) :percepts ( (self ?self) (segment ?seg) (line ?clane segment ?seg)) :relations ((driving-well-in-segment ?self ?seg ?clane) (last-lane ?clane) (not (lane-to-right ?clane ?anylane)))) ((driving-well-in-segment ?self ?seg ?lane) :percepts ((self ?self) (segment ?seg) (line ?lane segment ?seg)) :relations ((in-segment ?self ?seg) (in-lane ?self ?lane) (aligned-with-lane-in-segment ?self ?seg ?lane) (centered-in-lane ?self ?seg ?lane) (steering-wheel-straight ?self))) ((in-lane ?self ?lane) :percepts ( (self ?self segment ?seg) (line ?lane segment ?seg dist ?dist)) :tests ( (> ?dist -10) (<= ?dist 0))) ((in-segment ?self ?seg) :percepts ( (self ?self segment ?seg) (segment ?seg)))

17. ICARUS Skills for In-City Driving ((in-rightmost-lane ?self ?line) :percepts((self ?self) (line ?line)) :start ((last-lane ?line)) :subgoals ((driving-well-in-segment ?self ?seg ?line))) ((driving-well-in-segment ?self ?seg ?line) :percepts((segment ?seg) (line ?line) (self ?self)) :start ((steering-wheel-straight ?self)) :subgoals ((in-segment ?self ?seg) (centered-in-lane ?self ?seg ?line) (aligned-with-lane-in-segment ?self ?seg ?line) (steering-wheel-straight ?self))) ((in-segment ?self ?endsg) :percepts((self ?self speed ?speed) (intersection ?int cross ?cross) (segment ?endsg street ?cross angle ?angle)) :start ((in-intersection-for-right-turn ?self ?int)) :actions((steer 1)))

18. Representing Short-Term Beliefs/Goals (current-street me A) (current-segment me g550) (lane-to-right g599 g601) (first-lane g599) (last-lane g599) (last-lane g601) (at-speed-for-u-turn me) (slow-for-right-turn me) (steering-wheel-not-straight me) (centered-in-lane me g550 g599) (in-lane me g599) (in-segment me g550) (on-right-side-in-segment me) (intersection-behind g550 g522) (building-on-left g288) (building-on-left g425) (building-on-left g427) (building-on-left g429) (building-on-left g431) (building-on-left g433) (building-on-right g287) (building-on-right g279) (increasing-direction me) (buildings-on-right g287 g279)

19. Encoding Perceived Objects (self me speed 5 angle-of-road -0.5 steering-wheel-angle -0.1) (segment g562 street 1 dist -5.0 latdist 15.0) (line g564 length 100.0 width 0.5 dist 35.0 angle 1.1 color white segment g562) (line g565 length 100.0 width 0.5 dist 15.0 angle 1.1 color white segment g562) (line g563 length 100.0 width 0.5 dist 25.0 angle 1.1 color yellow segment g562) (segment g550 street A dist oor latdist nil) (line g600 length 100.0 width 0.5 dist -15.0 angle -0.5 color white segment g550) (line g601 length 100.0 width 0.5 dist 5.0 angle -0.5 color white segment g550) (line g599 length 100.0 width 0.5 dist -5.0 angle -0.5 color yellow segment g550) (intersection g522 street A cross 1 dist -5.0 latdist nil) (building g431 address 99 street A c1dist 38.2 c1angle -1.4 c2dist 57.4 c2angle -1.0) (building g425 address 25 street A c1dist 37.8 c1angle -2.8 c2dist 56.9 c2angle -3.1) (building g389 address 49 street 1 c1dist 49.2 c1angle 2.7 c2dist 53.0 c2angle 2.2) (sidewalk g471 dist 15.0 angle -0.5) (sidewalk g474 dist 5.0 angle 1.07) (sidewalk g469 dist -25.0 angle -0.5) (sidewalk g470 dist 45.0 angle 1.07) (stoplight g538 vcolor green hcolor red))

20. Hierarchical Structure of Long-Term Memory ICARUS organizes both concepts and skills in a hierarchical manner. concepts Each concept is defined in terms of other concepts and/or percepts. Each skill is defined in terms of other skills, concepts, and percepts. skills

21. concepts skills Hierarchical Structure of Long-Term Memory ICARUS interleaves its long-term memories for concepts and skills. For example, the skill highlighted here refers directly to the highlighted concepts.

22. Architectural Commitment to Processes In addition, a cognitive architecture makes commitments about: performance processes for: retrieval, matching, and selection inference and problem solving perception and motor control learning processes that: generate new long-term knowledge structures refine and modulate existing structures In most cognitive architectures, performance and learning are tightly intertwined.

23. Cognitive psychology makes clear claims about performance: Ideas about Performance • humans can handle multiple goals with different priorities, which can interrupt tasks to which attention returns later; • conceptual inference, which typically occurs rapidly and unconsciously, is more basic than problem solving; • humans often resort to means-ends analysis to solve novel, unfamiliar problems; • mental problem solving requires greater cognitive resources than execution of automatized skills; • problem solving often occurs in a physical context, with mental processing being interleaved with execution. ICARUS embodies these ideas in its performance mechanisms.

24. Perceptual Buffer ICARUS’ Functional Processes Short-Term Belief Memory Long-Term Conceptual Memory Conceptual Inference Perception Environment Skill Retrieval and Selection Long-Term Skill Memory Short-Term Goal Memory Problem Solving Skill Learning Skill Execution Motor Buffer

25. ICARUS’ Inference-Execution Cycle On each successive execution cycle, the ICARUS architecture: places descriptions of sensed objects in the perceptual buffer; infers instances of concepts implied by the current situation; finds paths through the skill hierarchy from top-level goals; selects one or more applicable skill paths for execution; invokes the actions associated with each selected path. ICARUS agents are teleoreactive (Nilsson, 1994) in that they are executed reactively but in a goal-directed manner.

26. Basic ICARUS Processes ICARUS matches patterns to recognize concepts and select skills. concepts Concepts are matched bottom up, starting from percepts. Skill paths are matched top down, starting from intentions. skills

27. ICARUS Interleaves Execution and Problem Solving Skill Hierarchy Problem Reactive Execution ? no impasse? Primitive Skills yes Executed plan Problem Solving

28. Solve(G) Push the goal literal G onto the empty goal stack GS. On each cycle, If the top goal G of the goal stack GS is satisfied, Then pop GS. Else if the goal stack GS does not exceed the depth limit, Let S be the skill instances whose heads unify with G. If any applicable skill paths start from an instance in S, Then select one of these paths and execute it. Else let M be the set of primitive skill instances that have not already failed in which G is an effect. If the set M is nonempty, Then select a skill instance Q from M. Push the start condition C of Q onto goal stack GS. Else if G is a complex concept with the unsatisfied subconcepts H and with satisfied subconcepts F, Then if there is a subconcept I in H that has not yet failed, Then push I onto the goal stack GS. Else pop G from the goal stack GS and store information about failure with G's parent. Else pop G from the goal stack GS. Store information about failure with G's parent. Interleaving Reactive Control and Problem Solving This is traditional means-ends analysis, with three exceptions: (1) conjunctive goals must be defined concepts; (2) chaining occurs over both skills/operators and concepts/axioms; and (3) selected skills are executed whenever applicable.

29. C B B A A C A Successful Problem-Solving Trace initial state (clear C) (hand-empty) (unst. C B) (clear B) (unstack C B) goal (on C B) (unst. B A) (clear A) (unstack B A) (ontable A T) (holding C) (hand-empty) (putdown C T) (on B A) (holding B)

30. Cognitive psychology has also developed ideas about learning: Claims about Learning • efforts to overcome impasses during problem solving can lead to the acquisition of new skills; • learning can transform backward-chaining heuristic search into more informed forward-chaining behavior; • learning is incremental and interleaved with performance; • structural learning involves monotonic addition of symbolic elements to long-term memory; • transfer to new tasks depends on the amount of structure shared with previously mastered tasks. ICARUS incorporates these assumptions into its basic operation.

31. Skill Hierarchy Problem ? ICARUS Learns Skills from Problem Solving Reactive Execution no impasse? Primitive Skills yes Executed plan Problem Solving Skill Learning

32. ICARUS’ Constraints on Skill Learning What determines the hierarchical structure of skill memory? The structure emerges the subproblems that arise during problem solving, which, because operator conditions and goals are single literals, form a semilattice. What determines the heads of the learned clauses/methods? The head of a learned clause is the goal literal that the planner achieved for the subproblem that produced it. What are the conditions on the learned clauses/methods? If the subproblem involved skill chaining, they are the conditions of the first subskill clause. If the subproblem involved concept chaining, they are the subconcepts that held at the subproblem’s outset.

33. C B B A A C Constructing Skills from a Trace (clear C) skill chaining 1 (hand-empty) (unst. C B) (clear B) (unstack C B) (on C B) (unst. B A) (clear A) (unstack B A) (ontable A T) (holding C) (hand-empty) (putdown C T) (on B A) (holding B)

34. C B B A A C Constructing Skills from a Trace (clear C) 1 (hand-empty) (unst. C B) (clear B) (unstack C B) (on C B) (unst. B A) (clear A) (unstack B A) skill chaining 2 (ontable A T) (holding C) (hand-empty) (putdown C T) (on B A) (holding B)

35. C B B A A C Constructing Skills from a Trace (clear C) concept chaining 3 1 (hand-empty) (unst. C B) (clear B) (unstack C B) (on C B) (unst. B A) (clear A) (unstack B A) 2 (ontable A T) (holding C) (hand-empty) (putdown C T) (on B A) (holding B)

36. C B B A A C Constructing Skills from a Trace skill chaining (clear C) 4 3 1 (hand-empty) (unst. C B) (clear B) (unstack C B) (on C B) (unst. B A) (clear A) (unstack B A) 2 (ontable A T) (holding C) (hand-empty) (putdown C T) (on B A) (holding B)

37. (clear (?C) :percepts ((block ?D) (block ?C)) :start ((unstackable ?D ?C)) :skills ((unstack ?D ?C)))(clear (?B) :percepts ((block ?C) (block ?B)) :start ((on ?C ?B) (hand-empty)) :skills ((unstackable ?C ?B) (unstack ?C ?B)))(unstackable (?C ?B) :percepts ((block ?B) (block ?C)) :start ((on ?C ?B) (hand-empty)) :skills ((clear ?C) (hand-empty)))(hand-empty ( ) :percepts ((block ?D) (table ?T1)) :start ((putdownable ?D ?T1)) :skills ((putdown ?D ?T1))) Learned Skills in the Blocks World Hierarchical skills are generalized traces of successful means-ends problem solving

38. Cumulative Curves for Blocks World

39. Cumulative Curves for Blocks World

40. Cumulative Curves for FreeCell

41. Learning Skills for In-City Driving We have also trained ICARUS to drive in our in-city environment. We provide the system with tasks of increasing complexity. Learning transforms the problem-solving traces into hierarchical skills. The agent uses these skills to change lanes, turn, and park using only reactive control.

42. ((parked ?me ?g1152) :percepts ( (lane-line ?g1152) (self ?me)) :start ( ) :subgoals ( (in-rightmost-lane ?me ?g1152) (stopped ?me)) )((in-rightmost-lane ?me ?g1152) :percepts ( (self ?me) (lane-line ?g1152)) :start ( (last-lane ?g1152)) :subgoals ( (driving-well-in-segment ?me ?g1101 ?g1152)) ) ((driving-well-in-segment ?me ?g1101 ?g1152) :percepts ( (lane-line ?g1152) (segment ?g1101) (self ?me)) :start ( (steering-wheel-straight ?me)) :subgoals ( (in-lane ?me ?g1152) (centered-in-lane ?me ?g1101 ?g1152) (aligned-with-lane-in-segment ?me ?g1101 ?g1152) (steering-wheel-straight ?me)) ) Skill Clauses Learning for In-City Driving

43. Learning Curves for In-City Driving

44. Transfer of Skills in ICARUS We are exploring such effects in ICARUS as part of a DARPA program on the transfer of learned knowledge. Testbeds include first-person shooter games, board games, and physics problem solving. The architecture also supports the transfer of knowledge in that: skills acquired later can build on those learned earlier; skill clauses are indexed by the goals they achieve; conceptual inference supports mapping across domains.

45. Transfer Effects in FreeCell On 16-card FreeCell tasks, prior training aids solution probability.

46. Transfer Effects in FreeCell However, it also lets the system solve problems with less effort.

47. Architectures as Programming Languages Cognitive architectures come with a programming language that: includes a syntax linked to its representational assumptions inputs long-term knowledge and initial short-term elements provides an interpreter that runs the specified program incorporates tracing facilities to inspect system behavior Such programming languages ease construction and debugging of knowledge-based systems. For this reason, cognitive architectures support far more efficient development of software for intelligent systems.

48. Programming in ICARUS The programming language associated with ICARUS comes with: a syntax for concepts, skills, beliefs, and percepts the ability to load and parse such programs an interpreter for inference, execution, planning, and learning a trace package that displays system behavior over time We have used this language to develop adaptive intelligent agents in a variety of domains.

49. An ICARUS Agent for Urban Combat

50. Intellectual Precursors ICARUS’ design has been influenced by many previous efforts: earlier research on integrated cognitive architectures especially ACT, Soar, and Prodigy earlier frameworks for reactive control of agents research on belief-desire-intention (BDI) architectures planning/execution with hierarchical transition networks work on learning macro-operators and search-control rules previous work on cumulative structure learning However, the framework combines and extends ideas from its various predecessors in novel ways.