Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California USA An Adaptive Architecture for Physical Agents Thanks to D. Choi, K. Cummings, N. Nejati, S. Rogers, S. Sage, D. Shapiro, and J. Xuan for their contributions. This talk reports research. funded by grants from DARPA IPTO and the US National Science Foundation, which are not responsible for its contents.

The original goal of artificial intelligence was to design and implement computational artifacts that:  combined many cognitive abilities in an integrated system;  exhibited the same level of intelligence as humans;  utilized its intelligence in a general way across domains. General Cognitive Systems Instead, modern AI has divided into many subfields that care little about systems, generality, or even intelligence. But the challenge remains and we need far more research on general cognitive systems.

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.

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

Newell’s Vision  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 application domain. However, there are different paths toward achieving these goals. In 1973, Allen Newell argued “You can’t play twenty questions with nature and win”. Instead, he proposed that we:

An Architecture with Communicating Modules action perception reasoning learning planning language software engineering / multi-agent systems

action perception reasoning learning planning language short-term beliefs and goals An Architecture with Shared Short-Term Memory blackboard architectures

Newell’s vision for research on theories of intelligence was that:  agent architectures should make strong theoretical assumptions about the nature of the mind.  architectural designs 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. Architectures and Constraints A successful architecture 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.

An Architecture with Shared Long-Term Memory action perception reasoning learning planning language short-term beliefs and goals long-term memory structures cognitive architectures

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

The I CARUS Architecture  physical agents that operate in an external environment;  the hierarchical structure of knowledge and its origin. In this talk I will use one such framework  I CARUS  to illustrate the advantages of cognitive architectures. Like previous candidates, it incorporates ideas from theories of human problem solving and reasoning. However, I CARUS is also distinctive in its concern with: These concerns have led to different assumptions than earlier cognitive architectures like ACT-R, Soar, and Prodigy.

Theoretical Claims of I CARUS 1.Cognition grounded in perception and action 2.Cognitive separation of categories and skills 3.Hierarchical organization of long-term memory 4.Cumulative learning of skill/concept hierarchies 5.Correspondence of long-term/short-term structures 6.Modulation of symbolic structures with numeric content Our recent work on I CARUS has been guided by six principles: These ideas distinguish I CARUS from most other architectures.

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. Architectural Commitment to Memories Each memory is responsible for different content that the agent uses in its activities.

I CARUS ’ Memories Long-TermConceptualMemory Long-Term Skill Memory Short-TermConceptualMemory Short-TermGoal/SkillMemory Environment PerceptualBuffer MotorBuffer

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. Architectural Commitment to Representations Most cognitive architectures rely upon formalisms similar to predicate calculus that express relational content. These build on the central assumption of AI that intelligence involves the manipulation of list structures.

I CARUS ’ Percepts are Objects with Attributes (self me speed 24.0 wheel-angle 0.02 limit 25.0 road-angle 0.06) (segment g1059 street 2 dist -5.0 latdist 15.0) (segment g1050 street A dist latdist nil) (segment g1049 street A dist oor latdist nil) (lane-line g1073 length width 0.5 dist 35.0 angle 1.57 color white) (lane-line g1074 length width 0.5 dist 15.0 angle 1.57 color white) (lane-line g1072 length width 0.5 dist 25.0 angle 1.57 color yellow) (lane-line g1100 length width 0.5 dist angle 0.0 color white) (lane-line g1101 length width 0.5 dist 5.0 angle 0.0 color white) (lane-line g1099 length width 0.5 dist -5.0 angle 0.0 color yellow) (lane-line g1104 length width 0.5 dist 5.0 angle 0.0 color white) (intersection g1021 street A cross 2 dist -5.0 latdist nil) (building g943 address 246 c1dist c1angle c2dist nil c2angle nil) (building g941 address 246 c1dist c1angle c2dist c2angle -0.73) (building g939 address 197 c1dist c1angle c2dist c2angle -2.10) (building g943 address 172 c1dist c1angle c2dist c2angle -2.53) (sidewalk g975 dist 15.0 angle 0.0) (sidewalk g978 dist 5.0 angle 1.57)

I CARUS ’ Beliefs are Relations Among Objects (not-on-street me g2980)(currrent-building me g2222) (not-approaching-cross-street me g2980)(not-on-cross-street me g2980) (current-street me A) (current-segment me g2480) (not-delivered g2980) (in-U-turn-lane me g2533) (in-leftmost-lane me g2533)(lane-to-right me g2533) (fast-for-right-turn me)(fast-for-U-turn me) (driving-in-segment me g2480 g2533)(at-speed-for-cruise me) (steering-wheel-straight me)(centered-in-lane me g2533) (aligned-with-lane me g2533)(in-lane me g2533) (on-right-side-of-road me)(in-segment me g2480) (buildings-on-right me g2231 g2230 g2480)(increasing me g2231 g2230 g2480) (buildings-on-right me g2231 g2222 g2480)(increasing me g2231 g2222 g2480) (buildings-on-right me g2231 g2211 g2480)(increasing me g2231 g2211 g2480) (buildings-on-right me g2230 g2222 g2480)(increasing me g2230 g2222 g2480) (buildings-on-right me g2230 g2211 g2480)(increasing me g2230 g2211 g2480) (buildings-on-right me g2222 g2211 g2480)(increasing me g2222 g2211 g2480) (buildings-on-left me g2366 g2480)(buildings-on-left me g2368 g2480) (buildings-on-left me g2370 g2480)(buildings-on-left me g2372 g2480)

Teleoreactive Logic Programs  Concepts: A set of conjunctive relational inference rules;  Primitive skills: A set of durative STRIPS operators;  Nonprimitive skills: A set of clauses which specify:  a head that indicates a goal the method achieves;  a set of (possibly defined) preconditions;  one or more ordered subskills for achieving the goal. These teleoreactive logic programs can be executed reactively but in a goal-directed manner (Nilsson, 1994). ICARUS encodes long-term knowledge of three general types:

I CARUS Concepts for In-City Driving (in-segment (?self ?sg) :percepts((self ?self segment ?sg) (segment ?sg))) (aligned-with-lane (?self ?lane) :percepts((self ?self) (lane-line ?lane angle ?angle)) :positives((in-lane ?self ?lane)) :tests((> ?angle -0.05) ( ?angle -0.05) (< ?angle 0.05)) ) (on-street (?self ?packet) :percepts((self ?self) (packet ?packet street ?street) (segment ?sg street ?street)) :positives((not-delivered ?packet) (current-segment ?self ?sg)) ) (increasing-direction (?self) :percepts((self ?self)) :positives((increasing ?b1 ?b2)) :negatives((decreasing ?b3 ?b4)) )

(on-street-right-direction (?self ?packet) :percepts((self ?self segment ?segment direction ?dir) (building ?landmark)) :start ((on-street-wrong-direction ?self ?packet)) (building ?landmark)) :start ((on-street-wrong-direction ?self ?packet)) :ordered((get-in-U-turn-lane ?self) (prepare-for-U-turn ?self) :ordered((get-in-U-turn-lane ?self) (prepare-for-U-turn ?self) (steer-for-U-turn ?self ?landmark)) ) (steer-for-U-turn ?self ?landmark)) ) (get-aligned-in-segment (?self ?sg) :percepts((lane-line ?lane angle ?angle)) :requires((in-lane ?self ?lane)) :effects((aligned-with-lane ?self ?lane)) :actions((  steer (  times ?angle 2))) ) (steer-for-right-turn (?self ?int ?endsg) :percepts((self ?self speed ?speed) (intersection ?int cross ?cross) (segment ?endsg street ?cross angle ?angle)) :start((ready-for-right-turn ?self ?int)) :effects((in-segment ?self ?endsg)) :actions((  times steer 2)) ) :actions((  times steer 2)) ) I CARUS Skills for In-City Driving

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

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

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 Architectural Commitment to Processes In most cognitive architectures, performance and learning are tightly intertwined.

I CARUS ’ Functional Processes Long-TermConceptualMemory Short-TermConceptualMemory Short-TermGoal/SkillMemory ConceptualInference SkillExecution Perception Environment PerceptualBuffer Problem Solving Skill Learning MotorBuffer Skill Retrieval Long-Term Skill Memory

The I CARUS Control Cycle 1.places descriptions of sensed objects in the perceptual buffer; 2.infers instances of concepts implied by the current situation; 3.finds paths through the skill hierarchy from top-level goals; 4.selects one or more applicable skill paths for execution; 5.invokes the actions associated with each selected path. On each successive execution cycle, the I CARUS architecture: Thus, I CARUS agents are examples of what Nilsson (1994) refers to as teleoreactive systems.

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

I CARUS Interleaves Execution and Problem Solving Executed plan Problem ? Skill Hierarchy Primitive Skills Reactive Execution impasse? Problem Solving yes no

Interleaving Reactive Control and Problem Solving 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. 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. 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.

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

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 Architectures as Programming Languages 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.

The programming language associated with I CARUS comes with:  the syntax of teleoreactive logic programs  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 Programming in I CARUS We have used this language to develop adaptive intelligent agents in a variety of domains.

The Origin of Skill Hierarchies  are consistent with knowledge of human behavior;  operate in an incremental and cumulative manner;  satisfy constraints imposed by other I CARUS components. I CARUS ’ commitment to hierarchical organization raises a serious question about the origin of its structures. We want mechanisms which acquire these structures in ways that: This requires some source of experience from which to create hierarchical structures.

I CARUS Learns Skills from Problem Solving Executed plan Problem ? Skill Hierarchy Primitive Skills Reactive Execution impasse? Problem Solving yes no Skill Learning

Three Questions about Skill Learning  What is the hierarchical structure of the network?  The structure is determined by the subproblems that arise in problem solving, which, because operator conditions and goals are single literals, form a semilattice.  What are 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 outset of the subproblem.

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

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

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

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

Learned Skills in the Blocks World Learned Skills in the Blocks World (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)))

Skill Clauses Learning for In-City Driving Skill Clauses Learning for In-City Driving (parked (?ME ?G1152) :percepts((lane-line ?G1152) (self ?ME)) :start( ) :skills((in-rightmost-lane ?ME ?G1152) (stopped ?ME)) ) (in-rightmost-lane (?ME ?G1152) :percepts((self ?ME) (lane-line ?G1152)) :start ((last-lane ?G1152)) :skills ((driving-in-segment ?ME ?G1101 ?G1152)) ) (driving-in-segment (?ME ?G1101 ?G1152) :percepts((lane-line ?G1152) (segment ?G1101) (self ?ME)) :start ((steering-wheel-straight ?ME)) :skills ((in-lane ?ME ?G1152) (centered-in-lane ?ME ?G1101 ?G1152) (aligned-with-lane-in-segment ?ME ?G1101 ?G1152) (steering-wheel-straight ?ME)) )

Learned Skill for Changing Lanes We have trained the I CARUS driving agent in a cumulative manner. First we present the system with the task of changing lanes using only primitive skills. The architecture calls on problem solving to handle the situation and caches the solution in a new composite skill. This skill later lets the agent change lanes in a reactive fashion.

Learned Preparation for Turning After the I CARUS agent has mastered changing lanes, we give it the more complex task of preparing for a turn. Again the system calls on problem solving, but this time it can use the skill already learned. As before, the solution is stored as a new skill, this one calling on the acquired skill for changing lanes.

Learned Turning and Parking Next we present the task of turning a corner and parking in the rightmost lane. As before, the system calls the problem solver to handle the situation and stores additional structures in memory. The resulting skill hierarchy can turn and park from the initial position using only reactive control.

Intellectual Precursors  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 I CARUS ’ design has been influenced by many previous efforts: However, the framework combines and extends ideas from its various predecessors in novel ways.

Directions for Future Research  forward chaining and mental simulation of skills;  learning expected utilities from skill execution histories;  learning new conceptual structures in addition to skills;  probabilistic encoding and matching of Boolean concepts;  flexible recognition of skills executed by other agents;  extension of short-term memory to store episodic traces. Future work on I CARUS should introduce additional methods for: Taken together, these features should make I CARUS a more general and powerful cognitive architecture.

Contributions of I CARUS  includes separate memories for concepts and skills;  organizes both memories in a hierarchical fashion;  modulates reactive execution with goal seeking;  augments routine behavior with problem solving; and  learns hierarchical skills in a cumulative manner. I CARUS is a cognitive architecture for physical agents that: These concerns distinguish I CARUS from other architectures, but it makes commitments along the same design dimensions.

Some Other Cognitive Architectures ACTSoarP RODIGY EPICRCS APEX C LARION Dynamic Memory Society of Mind CAPS GIPS 3T

Concluding Remarks  are embedded within a unified cognitive architecture;  incorporate modules that provide mutual constraints;  demonstrate a wide range of intelligent behavior;  are evaluated on multiple tasks in challenging testbeds. We need more research on integrated intelligent systems that: If you do not yet use a cognitive architecture in your research on intelligent agents, please consider it seriously. For more information about the I CARUS architecture, see:

End of Presentation

As traditionally defined and utilized, a cognitive architecture:  specifies the infrastructure that holds constant over domains, as opposed to knowledge, which varies.  models behavior at the level of functional structures and processes, not the knowledge or implementation levels.  commits to representations and organizations of knowledge and processes that operate on them.  comes with a programming language for encoding knowledge and constructing intelligent systems. Aspects of Cognitive Architectures Early candidates were cast as production system architectures, but alternatives have gradually expanded the known space.

In the Beginning... AI monobloc

The Big Bang Theory of AI action perception reasoning learning planning language 

The Task of In-City Driving

Transfer Effects in the Blocks World 20 blocks

Learning Curves for In-City Driving

FreeCell Solitaire FreeCell is a full-information card game that, in most cases, can be solved by planning; it also has a highly recursive structure.

Transfer Effects in FreeCell 16 cards

An Approach to Hierarchy Learning  decomposes complex problems into subproblems;  relies on heuristic search to find useful decompositions. We have extended I CARUS to incorporate a module for means-ends problem solving that: When I CARUS cannot execute a skill because its start condition is unmet, this mechanism:  chains backward off skills that would achieve the condition; or  chains backward off definitions of the unsatisfied concept. Traces of successful problem solving serve as the basis for new hierarchical structures.

Evaluation of Intelligent Systems Experimental studies of intelligent systems have lagged behind ones for component methods because:  they focus on more complex, multi-step behavior;  they require more engineering to develop them;  they rely on interaction among their components. Together, these factors have slowed the widespread adoption of experimental evaluation.

Repositories for Intelligent Systems Public repositories are now common among the AI subfields, and they offer clear advantages for research by:  providing fast and cheap materials for experiments;  supporting replication and standards for comparison. However, they can also produce undesirable side effects by:  focusing attention on a narrow class of problems;  encouraging a ‘bake-off ’ mentality among researchers. To support research on intelligent systems, we need testbeds and environments designed with them in mind.

Concluding Remarks We must also think about ways to overcome social obstacles to pursuing this research agenda:  conference tracks on integrated systems (e.g., AAAI)  testbeds that evaluate general intelligence (e.g., GGP) Both RoboCup and ICDL are in excellent positions to foster more work along these lines. I hope to see increased activity of this type at future meetings of these conferences.