Pat Langley School of Computing and Informatics Arizona State University Tempe, Arizona

1. A Cognitive Architecture for Integrated Intelligent Agents Pat Langley School of Computing and Informatics Arizona State University Tempe, Arizona 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. Claim 1: Integrated 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 integrated cognitive systems.

3. Claim 2: Psychology and Design Heuristics To develop intelligent systems, we must constrain their design, and findings about human behavior can suggest: how the system should represent and organize knowledge how the system should use that knowledge in performance how the system should acquire knowledge from experience This approach has led to many new insights and methods, but few modern AI researchers take advantage of it. We need far more work that incorporates ideas from cognitive psychology into the design of AI systems.

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

5. 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.

6. 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; 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.

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

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

9. 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, which were also to incorporate results from psychology.

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

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

12. 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.

13. Cascaded Integration in ICARUS ICARUSadopts a cascaded approach to integration in which lower-level modules produce results for higher-level ones. learning problem solving skill execution conceptual inference Each of these modules incorporates a variety of ideas that have their origin in cognitive psychology.

14. Cognitive psychology makes claims about conceptual knowledge: Representing and Using Concepts • concepts are distinct cognitive entities that support both categorization and inference; • the majority of human concepts are grounded in perception and action (Barsalou, 1999); • many human concepts are relational in nature, describing connections among entities (Kotovsky & Gentner, 1996); • concepts are organized in a hierarchical manner, with more complex categories defined in terms of simpler ones. ICARUS adopts these assumptions about conceptual memory.

15. 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)))

16. Structure and Use of Conceptual Memory ICARUS organizes conceptual memory in a hierarchical manner. Conceptual inference occurs from the bottom up, starting from percepts to produce high-level beliefs about the current state.

17. 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)

18. Psychology also makes claims about skills and their execution: Skills and Execution • the same generic skill may be applied to distinct objects that meet its application conditions; • skills support the execution of complex activities that have hierarchical organization (Rosenbaum et al., 2001); • humans can carry out open-loop sequences, but they can also operate in closed-loop reactive mode; • humans can deal with multiple goals with different priorities, which can lead to interrupted behavior. ICARUS embodies these ideas in its skill execution module.

19. 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)))

20. ICARUS Skills Build on Concepts ICARUS stores skills in a hierarchical manner that links to concepts. 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. Skill Execution in ICARUS Skill execution occurs from the top down, starting from goals to find applicable paths through the skill hierarchy. This process repeats on each cycle to give teleoreactive control (Nilsson, 1994) with a bias toward persistence of initiated skills.

22. Psychology also has ideas about problem solving and learning: Ideas about Problem Solving and Learning • humans often resort to means-ends analysis to solve novel problems (Newell & Simon, 1961); • problem solving often occurs in a physical context and is interleaved with execution (Gunzelman & Anderson, 2003); • efforts to overcome impasses during problem solving leads to incremental acquisition of new skills (Anzai & Simon, 1979); • structural learning involves monotonic addition of symbolic elements to long-term memory; • learning can transform backward-chaining heuristic search into informed forward-chaining execution (Larkin et al., 1980). ICARUS reflects these ideas in its problem solving and learning.

23. ICARUS Interleaves Execution and Problem Solving Skill Hierarchy Problem Reactive Execution ? no impasse? Primitive Skills yes Executed plan Problem Solving This organization reflects the psychological distinction between automatized and controlled behavior.

24. 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)

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

26. 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)

27. 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)

28. 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)

29. 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)

30. (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

31. Cumulative Curves for Blocks World

32. Cumulative Curves for FreeCell

33. 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.

34. ((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

35. Learning Curves for In-City Driving

36. 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.

37. Transfer Effects in FreeCell On tasks with more cards, prior training aids solution probability.

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

39. 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.

40. 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.

41. An ICARUS Agent for Urban Combat

42. Perceptual Buffer ICARUS’ Memories and 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

43. ICARUS has much in common with other cognitive architectures like Soar (Laird et al., 1987) and ACT-R (Anderson, 1993): Similarities to Previous Architectures Short-term memories are distinct from long-term stores Memories contain modular elements cast as symbolic structures Long-term structures are accessed through pattern matching Cognition occurs in retrieval/selection/action cycles Learning involves monotonic addition of elements to memory Learning is incremental and interleaved with performance These ideas all have their origin in theories of human memory, problem solving, and skill acquisition.

44. However, ICARUSalso makes assumptions that distinguish it from these architectures: Distinctive Features of ICARUS Cognition is grounded in perception and action Categories and skills are separate cognitive entities Short-term elements are instances of long-term structures Inference and execution are more basic than problem solving Skill/concept hierarchies are learned in a cumulative manner Some of these assumptions appear in Bonasso et al.’s (2003) 3T, Freed’s APEX, and Sun et al.’s (2001) CLARION architectures.

45. Directions for Future Research Future work on ICARUS should incorporate other ideas about: progressive deepening in forward-chaining search graded nature of categories and category learning model-based character of human reasoning persistent but limited nature of short-term memories creating perceptual chunks to reduce these limitations storing and retrieving episodic memory traces These additions will increase further ICARUS’ debt to psychology.

46. We need more research on integrated intelligent systems that: Concluding Remarks are embedded within a unified cognitive architecture; incorporate constraints based on ideas from psychology; demonstrate a wide range of intelligent behavior; are evaluated on multiple tasks in challenging testbeds. For more information about the ICARUS architecture, see: http://cll.stanford.edu/research/ongoing/icarus/

47. End of Presentation

48. 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.

49. 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.

50. Cumulative Curves for Blocks World