Unified Cognitive Architecture for Embodied Agents

1. A Unified Cognitive Architecture for Embodied Agents Pat Langley School of Computing and Informatics Arizona State University Tempe, Arizona USA Thanks to D. Choi, T. Konik, N. Li, D. Shapiro, and D. Stracuzzi for their contributions. This talk reports research partly funded by grants from DARPA IPTO, which is not responsible for its contents.

2. Cognitive Architectures • the memories that store domain-specific content • the system’s representation and organization of knowledge • the mechanisms that use this knowledge in performance • the processes that learn this knowledge from experience An architecture typically comes with a programming language that eases construction of knowledge-based systems. Research in this area incorporates many ideas from psychology about the nature of human thinking. A cognitive architecture (Newell, 1990) is the infrastructure for an intelligent system that is constant across domains:

3. The ICARUS Architecture ICARUS (Langley, 2006) is a computational theory of the human cognitive architecture that posits: 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 It shares these assumptions with other cognitive architectures like Soar (Laird et al., 1987) and ACT-R (Anderson, 1993).

4. Distinctive Features of ICARUS However, ICARUS also makes assumptions that distinguish it from these architectures: 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 tenets also appear in Bonasso et al.’s (2003) 3T, Freed’s (1998) APEX, and Sun et al.’s (2001) CLARION.

5. Cascaded Integration in ICARUS Like other unified cognitive architectures, ICARUS incorporates a number of distinct modules. learning problem solving skill execution conceptual inference ICARUS adopts a cascaded approach to integration in which lower-level modules produce results for higher-level ones.

6. Goals for ICARUS • a computational theory of higher-level cognition in humans • that is qualitatively consistent with results from psychology • that exhibits as many distinct cognitive functions as possible Although quantitative fits to specific results are desirable, they can distract from achieving broad theoretical coverage. Our main objectives in developing ICARUS are to produce:

7. An ICARUS Agent for Urban 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.

8. 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)))

9. 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.

10. 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)

11. 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)))

12. 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

13. 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.

14. Execution and Problem Solving in ICARUS Skill Hierarchy Problem Reactive Execution ? no impasse? Primitive Skills yes Executed plan Problem Solving Problem solving involves means-ends analysis that chains backward over skills and concept definitions, executing skills whenever they become applicable.

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

16. Learning from Problem Solutions ICARUS incorporates a mechanism for learning new skills that: operates whenever problem solving overcomes an impasse incorporates only information available from the goal stack generalizes beyond the specific objects concerned depends on whether chaining involved skills or concepts supports cumulative learning and within-problem transfer This skill creation process is fully interleaved with means-ends analysis and execution. Learned skills carry out forward execution in the environment rather than backward chaining in the mind.

17. 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

18. An ICARUS Agent for Urban Combat

19. ICARUS is a unified theory of the cognitive architecture that: ICARUS Summary includes hierarchical memories for concepts and skills; interleaves conceptual inference with reactive execution; resorts to problem solving when it lacks routine skills; learns such skills from successful resolution of impasses. We have developed ICARUS agents for a variety of simulated physical environments, including urban driving. However, it has a number of limitations that we must address to improve its coverage of human intelligence.

20. ICARUS indexes skills by the goals they achieve; this aids in: Challenge 1: Arbitrary Behaviors But these goals can describe only instantaneous states of the environment, which limits ICARUS’ representational power. For example, it cannot encode skills for complex dance steps that end where they start or the notion of a round trip. Retrieving relevant candidate skills for execution Determining when skill execution should terminate Constructing new skills from successful solutions

21. To support richer skills, we are extending ICARUS to include: Incorporating Temporal Constraints Concepts that indicate temporal relations which must hold among their subconcepts Skills that use these temporally-defined concepts as their goals and subgoals A belief memory that includes episodic traces of when each concept instance began and ended We are also augmenting its inference, execution, and learning modules to take advantage of these temporal structures.

22. The Concept of a Round Trip ((round-trip ?self ?a ?b) :percepts ((self ?self) (location ?a) (location ?b)) :relations ((at ?self ?a) ?start1 ?end1 (at ?self ?b) ?start2 ?end2 (at ?self ?a) ?start3 ?end3) :constraints ((≤ ?end1 ?start2) (≤ ?end2 ?start3))) Any round trip from A to B involves: • First being located at place A • Then being located at place B • Then being located at place A again We can specify this concept in the new formalism as:

23. Episodes and Skills for Round Trips [(at me loc1) 307 398] [(home loc1) 200 …] [(in-transit me loc1 loc2) 399 422] [(office loc2) 220 …] [(at me loc2) 422 536] [(at me loc1) 558 …] The execution module compares these to extended skills like: ((round-trip ?self ?a ?b) :percepts ((self ?self) (location ?a) (location ?b)) :start ((at ?self ?a) ?start1 ?end1) :subgoals ((at ?self ?b) ?start2 ?end2 (at ?self ?a) ?start3 ?end3) :constraints ((≤ ?end1 ?start2) (≤ ?end2 ?start3))) This checks their heads and uses constraints to order subgoals. The inference module automatically adds episodic traces like:

24. ICARUS currently acquires new hierarchical skill clauses by: Challenge 2: Robust Learning However, this mechanism has two important limitations: Solving novel problems through means-ends analysis Analyzing the steps used to achieve each subgoal Storing one skill clause for each solved subproblem • It can create skills with overly general start conditions • It depends on a hand-crafted hierarchy of concepts We hypothesize that a revised mechanism which also learns new concepts can address both of these problems.

25. To support better skill learning, we are extending ICARUS to: Forming New Concepts Create new conceptual predicates and associated definitions for start conditions and effects of acquired skills That are functionally motivated but structurally defined That extend the concept hierarchy to support future problem solving and skill learning Learned concepts for skills’ preconditions serve as perceptual chunks which access responses that achieve the agent’s goals.

26. D C B B A A C D Learning Concepts in the Blocks World When the problem solver achieves a goal, it learns both a new skill and two concepts, one for its preconditions and one for effects. The system uses a mechanism similar to that in composition (Neves & Anderson, 1981) to determine the conditions for each one. ICARUS uses the same predicate in two clauses if the achieved goals are the same and if the initially true subconcepts are the same (for concept chaining) or the utilized skills are the same (for skill chaining). (clear A) (unstacked B A) (unstackable B A) (on B A) (clear B) (hand-empty)    (clear C) This produces disjunctive and recursive concepts. (unstacked D C) (unstackable D C)

27. D C B B A A C D Learning Concepts in the Blocks World ICARUS solves novel problems in a top-down manner, using means-ends analysis to chain backward from goals. But it acquires concepts from the bottom up, just as it learns skills. Here it defines the base case for the start concept associated with the skill for making a block clear. (clear A) (unstacked B A) (unstackable B A) (on B A) (clear B) (hand-empty)    ((scclear ?C) :percepts ((block ?C) (block ?D)) :relations ((unstackable ?D ?C))) (clear C) (unstacked D C) (unstackable D C)

28. D C B B A A C D Learning Concepts in the Blocks World This process continues upward as the architecture achieves higher-level goals. Here ICARUS defines the recursive case for the start concept associated with the skill for making a block clear. (clear A) (unstacked B A) (unstackable B A) ((scclear ?B) :percepts ((block ?B) (block ?C)) :relations ((scunstackable ?C ?B))) (on B A) (clear B) (hand-empty)    ((scclear ?C) :percepts ((block ?C) (block ?D)) :relations ((unstackable ?D ?C))) (clear C) (unstacked D C) (unstackable D C)

29. D C B B A A C D Learning Concepts in the Blocks World Skills acquired with these learned concepts appear to be more accurate than those created with ICARUS’ old mechanism. (clear A) (unstacked B A) ((scunstackable ?B ?A) :percepts ((block ?B) (block ?A)) :relations ((on ?B ?A) (hand-empty) (scclear ?B))) (unstackable B A) ((scclear ?B) :percepts ((block ?B) (block ?C)) :relations ((scunstackable ?C ?B))) (on B A) (clear B) (hand-empty)    ((scclear ?C) :percepts ((block ?C) (block ?D)) :relations ((unstackable ?D ?C))) (clear C) (unstacked D C) (unstackable D C)

30. D C B B A A C D Learning Concepts in the Blocks World ((scclear ?A) :percepts ((block ?A) (block ?B)) :relations ((scunstackable ?B ?A))) (clear A) (unstacked B A) ((scunstackable ?B ?A) :percepts ((block ?B) (block ?A)) :relations ((on ?B ?A) (hand-empty) (scclear ?B))) (unstackable B A) ((scclear ?B) :percepts ((block ?B) (block ?C)) :relations ((scunstackable ?C ?B))) (on B A) (clear B) (hand-empty)    ((scclear ?C) :percepts ((block ?C) (block ?D)) :relations ((unstackable ?D ?C))) (clear C) (unstacked D C) (unstackable D C)

31. Benefits of Concept Learning (Free Cell)

32. Benefits of Concept Learning (Logistics)

33. ICARUS is designed to model intelligent behavior in embodied agents, but our work to date has treated them in isolation. Challenge 3: Reasoning about Others But people can reason more deeply about the goals and actions of others, then use their inferences to make decisions. The framework can deal with other independent agents, but only by viewing them as other objects in the environment. • Adding this ability to ICARUS will require knowledge, but it may also demand extensions to the architecture.

34. An Urban Driving Example You are driving in a city behind another vehicle when a dog suddenly runs across the road ahead of it. You do not want to hit the dog, but you are in no danger of that, yet you guess the other driver shares this goal. You reason that, if you were in his situation, you would swerve or step on the brakes to avoid hitting the dog. This leads you to predict that the other car may soon slow down very rapidly. Since you have another goal – to avoid collisions – you slow down in case that event happens.

35. For ICARUS to handle social cognition of this sort, it must: Social Cognition in ICARUS Imagine itself in another agent’s physical/social situation; Infer the other agent’s goals either by default reasoning or based on its behavior; Carry out mental simulation of the other agent’s plausible actions and their effects on the world; Take high-probability trajectories into account in selecting which actions to execute itself. Each of these abilities require changes to the architecture of ICARUS, not just its knowledge base.

36. In response, we are planning a number of changes to ICARUS: Architectural Extensions Add abductive reasoning that makes plausible inferences about goals – via relational cascaded Bayesian classifier Extend the problem solver to support forward-chaining search – via mental simulation using repeated lookahead Revise skill execution to consider probability of future events – using the desirability of likely trajectories These extensions will let ICARUS agents reason about other agents and use the results to influence its own behavior.

37. Although humans can reason explicitly about other agents’ likely actions, they gradually compile responses and automate them. The ICARUS skill learning module should achieve this effect by: Automating Social Cognition Treating goals achieved via anticipation as solved impasses; Analyzing steps that led to this solution to learn new skills; Using these skills to automate behavior when the agent finds itself in a similar situation. Over time, the agent will behave in socially relevant ways with no need for explicit reasoning or mental simulation.

38. ICARUS is a unified theory of cognition that exhibits important human abilities but that also has limitations. However, our recent work has extended the architecture to: Concluding Remarks Represent concepts and skills with temporal relations and use them to execute arbitrary behaviors; Acquire new predicates that extend the concept hierarchy and enable better skill learning; Reason about other agents’ situations and goals, predict their behavior, and select appropriate responses. These extensions bring ICARUS a few steps closer to a broad-coverage theory of higher-level cognition.

39. End of Presentation