Computational Discovery of Communicable Knowledge

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Computational Discovery of Communicable Knowledge Powered By Docstoc
					                   Cognitive Architectures and
                    Virtual Intelligent Agents

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

Thanks to D. Choi, G. Cleveland, T. Konik, N. Li, N. Nejati, C. Park, and D. Stracuzzi
for their contributions. This talk reports research partly funded by grants from ONR and
DARPA, which are not responsible for its contents.
                  The Nature of Intelligence

Cognitive science and AI, although distinct fields, agree on
some core assumptions:

 Intelligence arises from computational processes
 A variety of functional abilities underlie intelligence
 These abilities are general yet benefit from knowledge
 Humans remain our best examples of intelligent systems

Progress toward understanding intelligence requires taking a
position on two key methodological questions:
• What theoretical framework should one assume?
• What problems or testbeds should drive research?
            Frameworks for Intelligent Systems

Because intelligence involves distinct abilities, it is tempting
to develop them separately and then combine them.
This position is well represented in the AI community by:

 Software engineering (e.g., Brugali, 2009)
 Blackboard systems (Engelmore & Morgan, 1988)
 Multi-agent systems (Weiss, 2000)

Such integrated approaches offer clear advantages in terms of
modular development and division of labor.
But they are not the only way to create intelligent artifacts.
                   Cognitive Architectures

A cognitive architecture (Newell, 1990) is an infrastructure
for intelligent systems that:
 makes strong theoretical assumptions about the representations
  and mechanisms underlying cognition
 incorporates many ideas from psychology about the nature
  of the human mind
 contains distinct modules, but these access and alter the same
  memories and representations
 comes with a programming language that eases construction of
  knowledge-based systems
A cognitive architecture is all about mutual constraints, and it
should provide a unified theory of intelligent behavior.
              Testbeds for Intelligent Systems

We also need problems and testbeds that pose challenges to
theories and evaluate their responses. Examples include:

 Educational tasks (e.g., reasoning in math and physics)
 Standardized tests (e.g., IQ, SAT, GRE exams)
 Integrated robots (e.g., for search and rescue)
 Synthetic characters in virtual worlds (e.g., for games)

Each problem class has advantages and disadvantages, but all
have important roles to play.
In this talk, I will report work on synthetic characters that has
driven our progress on cognitive architectures.
Synthetic Agents for Urban Driving

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                     Outline for the Talk

• Frameworks and testbeds for intelligent systems
• Review of the ICARUS cognitive architecture
 • Common and distinctive features
 • Representation and organization of memory
 • Performance and learning mechanisms
• Synthetic agents developed with ICARUS
 • Complex synthetic environments
 • Lessons learned from these efforts
• Plans for future architectural / testbed research
                  The ICARUS Architecture

ICARUS (Langley, 2006) is a computational theory of the human
cognitive architecture that posits:
1. Short-term memories are distinct from long-term stores
2. Memories contain modular elements cast as symbolic structures
3. Long-term structures are accessed through pattern matching
4. Cognition occurs in retrieval/selection/action cycles
5. Learning involves monotonic addition of elements to memory
6. Learning is incremental and interleaved with performance

These assumptions are not novel; it shares them with architectures
like Soar (Laird et al., 1987) and ACT-R (Anderson, 1993).
              Distinctive Features of ICARUS

However, ICARUS also makes assumptions that distinguish it from
these architectures:

1. Cognition is grounded in perception and action
2. Categories and skills are separate cognitive entities
3. Short-term elements are instances of long-term structures
4. Skills and concepts are organized in a hierarchical manner
5. Inference and execution are more basic than problem solving

Some of these tenets also appear in Bonasso et al.’s (2003) 3T,
Freed’s (1998) APEX, and Sun et al.’s (2001) Clarion.
But only ICARUS combines them into a unified cognitive theory.
                 Research Goals for ICARUS

Our current objectives in developing ICARUS are to produce:

 a computational theory of high-level cognition in humans
 that is qualitatively consistent with results from psychology
 that exhibits as many distinct cognitive functions as possible
 that supports creation of intelligent agents in virtual worlds

We have not yet modeled quantitative results from experiments,
but we hope to obtain such fits when ICARUS is more mature.
This would enable more direct comparison to architectures like
ACT-R, Clarion, and EPIC (Meyer & Kieras, 1997).
             Cascaded Integration in ICARUS
Like other unified cognitive architectures, ICARUS incorporates a
number of distinct modules.


                        problem solving

                         skill execution

                      conceptual inference

ICARUS adopts a cascaded approach to integration in which
lower-level modules produce results for higher-level ones.
      ICARUS Beliefs and Goals for Urban Driving
Inferred beliefs:
(current-street me A)              (current-segment me g550)
(lane-to-right g599 g601)          (first-lane g599)
(last-lane g599)                   (last-lane g601)
(under-speed-limit 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)          (near-pedestrian me g567)
Top-level goals:
(not (near-pedestrian me ?any))    (not (near-vehicle me ?other))
(on-right-side-in-segment me)      (in-lane me ?segment)
(not (over-speed-limit me))        (not (running-red-light me))
           ICARUS Concepts for Urban 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)))
         Hierarchical Organization of Concepts
ICARUS organizes conceptual memory in a hierarchical manner.
                                                     concept clause

The same conceptual predicate can appear in multiple clauses
to specify disjunctive and recursive concepts.
             Conceptual Inference in ICARUS
Conceptual inference in ICARUS occurs from the bottom up.
                                                       concept clause

Starting with observed percepts, this process produces high-level
beliefs about the current state.
             Conceptual Inference in ICARUS
Conceptual inference in ICARUS occurs from the bottom up.
                                                       concept clause

Starting with observed percepts, this process produces high-level
beliefs about the current state.
             Conceptual Inference in ICARUS
Conceptual inference in ICARUS occurs from the bottom up.
                                                       concept clause

Starting with observed percepts, this process produces high-level
beliefs about the current state.
             Conceptual Inference in ICARUS
Conceptual inference in ICARUS occurs from the bottom up.
                                                       concept clause

Starting with observed percepts, this process produces high-level
beliefs about the current state.
              ICARUS Skills for Urban 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)))
           Hierarchical Organization of Skills
ICARUS organizes skills in a hierarchical manner, which each
skill clause indexed by the goal it aims to achieve.
                                                        skill clause

The same goal can index multiple clauses to allow disjunctive,
conditional, and recursive procedures.
                  Skill Execution in ICARUS
Skill execution occurs from the top down, starting from goals,
to find applicable paths through the skill hierarchy.
                                                              skill clause

A skill clause is applicable if its goal is unsatisfied and if its
conditions hold, given bindings from above.
                Skill Execution in ICARUS
This process repeats on each cycle to produce goal-directed but
reactive behavior (Nilsson, 1994).
                                                        skill clause

However, ICARUS prefers to continue ongoing skills when they
match, giving it a bias toward persistence over reactivity.
                 Skill Execution in ICARUS
If events proceed as expected, this iterative process eventually
achieves the agent’s top-level goal.
                                                          skill clause

At this point, another unsatisfied goal begins to drive behavior,
invoking different skills to pursue it.
       Execution and Problem Solving in ICARUS
  Skill Hierarchy
                                     no                     Problem


                                                        Generated Plan
  Primitive Skills


Problem solving involves means-ends analysis that chains backward over skills
and concept definitions, executing skills whenever they become applicable.
     ICARUS Learns Skills from Problem Solving
Skill Hierarchy
                          no             Problem


                                     Generated Plan
Primitive Skills


             Learning from Problem Solutions
ICARUS incorporates a mechanism for learning new skills that:

 operates whenever problem solving overcomes an impasse
 incorporates only information stored locally with goals
 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.
                       ICARUS Summary
ICARUS is a unified theory of the cognitive architecture that:

 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.
However, each effort has revealed limitations that have led to
important architectural extensions.
 MadRTS     Urban Combat

Rush 2008   Urban Driving
                            Creating Synthetic Agents in ICARUS
           Synthetic Agents for ‘Urban Combat’
Urban Combat is a synthetic environment, built on the Quake
engine, used in the DARPA Transfer Learning program.
Tasks for agents involved traversing an urban landscape with a
variety of obstacles to capture a flag.
Our experiences in Urban Combat provided some clear lessons:
 Storing, using, and learning route knowledge
 Learning to handle obstacles through
  trial and error
 Supporting different varieties of
  structural transfer across problems
Each of these insights has influenced
our more recent work on ICARUS.
                 Transfer Studies with Urban Combat
                                                   Experiments with ICARUS agents
                                                   on Urban Combat demonstrated
                                                   varieties of structural transfer
                                                   across multiple goal-directed tasks
IED                                                (Choi et al., ICCM-2007).

                                                    Transfer vs. Control Conditions


            Synthetic Agents for Urban Driving
We have developed an urban driving environment using Garage
Games’ Torque game engine.

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This domain involves a mixture of cognitive and sensori-motor
behavior in a constrained but complex and dynamic setting.
            Synthetic Agents for Urban Driving
The Torque urban driving environment supports tasks of graded
complexity, including:
 Keeping the car aligned at a constant speed
 Changing speed to obey traffic signs and lights
 Changing lanes/speed to avoid vehicles/pedestrians
 Approaching and turning a corner
 Driving repeatedly around a block
 Delivering packages to specified addresses
We have developed ICARUS agents that address each of these
tasks in a reasonably robust manner.
We have also demonstrated its ability to learn driving skills from
problem solving (Langley & Choi, JMLR, 2006).
            Synthetic Agents for Urban Driving
Our early efforts on urban driving motivated two key features
of the ICARUS architecture:
 Indexing skills by the goals they achieve
 Multiple top-level goals with priorities
Recent work on Torque driving agents has led to additional
changes to the architecture (Choi, 2010):
 Long-term memory for generic goals
 Reactive generation of specific short-term goals
 Resource-based execution of multiple skills per cycle

Our use of this complex environment has driven our cognitive
architecture research toward important new functionalities.
          Synthetic Agents for American Football
We have also developed ICARUS agents to execute football plays in
Rush 2008 from Knexus Research.

This domain is
less complex in
some ways but
it still remains
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among players
on a team.
          Synthetic Agents for American Football
Our results in Rush 2008 are interesting because ICARUS learned
its football skills by observing other agents.

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This and similar videos of the Oregon State football team provided
training examples to drive this process.
         Synthetic Agents for American Football

Our efforts on Rush 2008 have motivated additional extensions
to the ICARUS architecture:
 Time stamps on beliefs to support episodic traces
 Ability to represent and recognize temporal concepts
 Adapting means-ends analysis to explain observed behavior
 Ability to control multiple agents in an environment

We have used the extended ICARUS to learn hierarchical skills for
20 distinct football plays (Li et al., AAIDE-2009).
These results utilized Oregon State’s image-processing system,
which extracted objects and simple events from the videos.
            Synthetic Agents for Twig Scenarios
Most recently, we have used Horswill’s (2008) Twig simulator
to develop humanoid ICARUS agents.

This low-fidelity environment supports a few object types, along
with simple reactive behaviors for virtual characters.
            Synthetic Agents for Twig Scenarios
We have developed a specific Twig scenario that involves four
types of synthetic characters:
 Innocents go from ball to ball, pausing to pick up any dolls they
  encounter nearby.
 Collectors sit in chairs, buy dolls from anyone who offers at the
  right price, and secure these purchases in safes.
 Producers stand by trees, produce new dolls, and guard them by
  blocking any other agents who approach.
 Capitalists work in pairs, buying or stealing dolls from Innocents,
  stealing them from Producers, and selling them to Collectors.
We have developed ICARUS agents for these character types to
demonstrate their interactions with each other.
This served as the final project for an ASU course on cognitive
systems and intelligent agents (
Synthetic Agents for Twig Scenarios

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                  Reasoning about Others

We designed ICARUS to model intelligent embodied agents, but
our work has emphasized independent action.
• The framework can deal with other agents, but only by
  viewing them as objects in the environment.
Yet humans can reason about the beliefs, goals, and intentions of
others, then use their inferences to make decisions.

• One might even argue that such deep social cognition is a key
  feature of human intelligence.
Adding this capability to ICARUS will require extensions to its
representation, performance processes, and learning methods.
               Ongoing ICARUS Extensions

To provide ICARUS with the capability to reason about others’
mental states, we are:
• Extending its representation to allow embedded modal literals
  [e.g., (belief me (goal driver2 (in-right-lane driver2 seg6)))];
• Revising inference to support incremental abductive reasoning
  that explains events in terms of mental states;
• Extending skill execution and problem solving to pursue goals
  that change mental states (e.g., by communication); and
• Introducing learning mechanisms that make inference about
  others more efficient with experience.
We are developing these abilities to construct conversational
agents for emergency medical assistance in the field.
    Synthetic Environments / Architectural Testbeds

In an STTR collaboration, we are using SET Corp.’s CASTLE,
a flexible physics/simulation engine, to:
• Create an improved urban driving environment that supports a
  richer set of objects, goals, and activities;
• Develop a ‘treasure hunt’ testbed in which players must follow
  complex instructions and coordinate with other agents;
• Create interfaces to ICARUS, ACT-R, and other architectures;
• Design tasks of graded complexity that can aid in evaluating
  architecture-based cognitive models.
Together, these will let the community evaluate its architectures
on common problems in shared testbeds.
These will support both extended, open-ended operations and
brief, hand-crafted scenarios.
         CASTLE Screen Shots / Assumptions

Rigid body dynamics — solid objects, Newtonian physics, friction, joints,
springs; no flexible objects or fluids
Z-buffer graphics — solid objects, textures, lights; no shadows, motion
blur, or irradiance
                     Concluding Remarks

In this talk, I presented ICARUS, a unified theory of the human
cognitive architecture that:

• Grounds high-level cognition in perception and action
• Treats categories and skills as separate cognitive entities
• Views short-term structures as instances of long-term ones
• Organizes skills and concepts in a hierarchical manner
• Combines reactive control with deliberative problem solving
In addition, I reported a number of ICARUS agents that control
synthetic agents in simulated environments.
These efforts raised theoretical challenges, led to extensions, and
revealed insights about the nature of intelligence.
            Other Research on Modeling / Simulation

                                                                    Qualitative system-level
                                                                    models of human aging
                                                                    (Langley, NIA-2009)
                                               junk-protein                              junk-protein
Quantitative process models     –
                                                     Fe             Lysosome              Cytoplasm

of ecological systems                            +
                                                               +                     +
                                                 ROS               membrane-damage
(Bridewell et al., MLj, 2008)                    +
                                            oxidized-protein                              lipofuscin
                                               lipofuscin            lytic-enzyme

                                                 H202                                       H202
End of Presentation