gtrans: mixed-initiative planning system

GTrans: Mixed-Initiative Planning System

by Michael CoxReviewed by

Vu Le

IT 803 Spring 2004 – Mixed-Initiative Intelligent Systems – Prof. G. Tecuci

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Presentation Outline

Introduction to GTrans Planning as goal transformation. GTrans: a system using goal

transformation Example of using GTrans in planning Details of GTrans Lesson learned References

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Typical Planning Models: Search

In AI community, one of the most dominant models of planning is search, as in Protégé.

Planning consists of searching from initial state to goal state via sequence of operators.

Pre-cond – Operator(params) – Effect Example: blockworld

BCA

BC

A

BCA

B CA CBA

Initial state Final state (Goal)

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Typical Planning Models: Hierarchical Task Decomposition

The other common model is hierarchical task decomposition, as in Disciple.

The planning task is successively decomposed into subtasks until they are elementary tasks.

Find the solutions for simplest tasks.

Successively compose the solutions until solution to the initial task is obtained.

S1

S11 S1n

S111 S11mT11mT111

T1nT11

T1

…

…

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The Alternative Approach:Planning as Goal Transformation

The “alternative” is to model planning as goal transformation.

Planning involves moving goals through a problem space to reach equilibrium between available resources & constraints of dynamic environment.

This approach is still using search in the background and hiding all details of search from user.

The user can steer the goal around the problem space with the help from system to find the best possible solutions.

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Planning as Goal Transformation

Equilibrium between resource and constraints: enough resource & constraints are preserved. Constraints violation

Goal is to deliver package to a recipient at certain address. Recipient is no longer at that address (constraint violated). Goal change: return the package to sender.

Not enough resource Goal is to destroy the bridge. Not enough dynamites to fulfill the task (not enough

resource). Goal change: to reduce the transportation through the

bridge such as damage part of the bridge.

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The motivation of modeling planning as goal transformation is Cognitive model of planning as search is not

intuitive for human because Human planner typically jumps around based on

existing knowledge rather than systematically searches the state space.

BCA

BC

A

BCA

B CA CBA

Initial state Final state (Goal)

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The other motivation: Being able to understand planning details

requires amount of knowledge in planning technology such as pre-conditions, post-conditions and operators with variables bound.

Naïve users feel overwhelmed by details of planning as search.

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Introduction to GTrans Planning as goal transformation. GTrans: a system using goal

transformation Example of using GTrans in planning Details of GTrans Lesson learned References

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GTrans Mixed-initiative Planning System. Using goal transformation approach. Using Prodigy planning system in backend.

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Presentation Outline Introduction to GTrans Example of using GTrans in

planning Create scenario Step-by-step demonstration

Details of GTrans Lesson learned References

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Create Scenario Create scenario is the first

step in planning. Create scenario loads a

domain and sets up the objects that belong to domain.

Load domain: Military File/LoadDomain/Military: A

map comes up with no object. Map has 2 rivers R1, R2 and

bridges over them. There is a town & 2 airports.

Goal of this exercise is to prevent movement across rivers.

When user loads a domain, the system offers the relevant objects in that domain for user to select.

Military domain: Objects: fighter jets, warships, missiles…

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Create Scenario

The map has details but they are just images. Objects that represent them are needed to interact

with GTrans. Objects have properties: F15 can destroy only 1

bridge and can damage any bridge. Create objects: mobile v.s stationary objects. Mobile objects are drag-and-drop enabled: airplanes,

ships, people. Stationary objects are not: bridges, rivers, towns,

airports. This scenario needs F15s to destroy bridges. Create 5 bridges over 2 rivers, 4 F15s, 1 police, 1

infantry.

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Set Initial States Select State/Initial States. Once the user selects

initial state, the system suggests lists of initial states associated with objects.

User can set the state of objects by right-click on them.

Examples: F15_1 is ready Airport_1 is close to river_2 Bridge_1 enables

movement over River_1

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Set Goals Select State/Final States. System helps set up the goals

by providing lists of goals associated with objects.

There are 2 ways to set goal: Right-click on target, a popup menu

with goals associated with objects. Select the desired goal. Bridge: building bridge, destroy bridge.

Assign resource such as mobile object to target object by dragging:

Right-click on target, a list of goals associated with targets and resources comes up. Select the desired goal.

Drag F15 to bridge -> goal: destroy bridge by F15

The goal of this scenario is to make River_1 impassable

The letter “G” will be asserted at the target.

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Run Planner

Click on Planning/Run to start the planner. There will be some types of message

returned from planner such as: Done or No Plan with explanations.

No plan to blow up all 5 bridges with 4 F15s. Need goal transformation. System helps user transform the goal by

offering a hierarchical trees of predicates in particular domain. is-destroyed is-damaged Impassable restricted-movement.

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Transform Goal

Click on Planning/Change Goals to start the transform. Select current goal and change it. Change from “outcome-impassable” to “outcome-

restrict-movement”.

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The Final Plan

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Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans

Architecture Communication Control Shared awareness Tasks Evaluation

Lesson learned References

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Architecture

RMI KQML

• Three-layer architecture:GTrans RMI serverPlanning user interface (PUI)Prodigy/Agent

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Architecture GTrans RMI server and Planning UI use RMI

mechanism to communicate. Planning UI and Prodigy/Agent use KQML to

exchange information. GTrans server allows multiple users to co-

plan. Planning UI coordinated with Prodigy/Agent

allows users to create objects, set or modify goals, solve problems…

Prodigy/Agent allows underlying Prodigy to communicate with Planning UIs.

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GTrans Server Users log in/out to/from

GTrans server during planning.

A user shares planning information to other users via GTrans server in joint/share planning mode.

The user uses PUI to communicate with system.

The interactions of Gtrans server and Planning UIs follows publisher-subscribers pattern.

A PUI subscribes to the publisher (GTrans server) when user logs in to the system.

An event generates from PUI will be distributed to subscribed PUIs via publisher.

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Planning User Interface (PUI)

This is the user’s workplace where menu-based mixed-initiative transactions happen.

Users can setup goals, assign resources, change goals with agent assistance.

There are two types of transactions from Planning UI To GTrans Server: RMI To Prodigy/Agent: KQML

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Prodigy/Agent

Consists of Prodigy Planner and wrapper Prodigy Planner is a domain independent,

state-space planner. Prodigy uses backward & forward search

from initial states to goal. Prodigy domains are represented with a

conceptual hierarchy and a set of operators.

Wrapper serves as interface between Prodigy planner and Planning UI.

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Communication

There are 2 types of communication: Among users Between user and agent

Communication between users is realized by GTrans RMI server.

Communication between user and agent is realized by Prodigy wrapper.

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Human Users Communication

RMI

KQML

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Registration Service GTrans RMI Server offers registration

service Each Planning UI connects to GTrans

server has to register. Each Planning UI disconnects to

GTrans server has to unregister. Registration service monitors all

connected clients and allows interaction between clients.

Planning UI connects to server via Java RMI.

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Registration Service

GTrans RMIServer Planning UI

subscribe

acknowledge

create event

acknowledge

distribute event

acknowledge

unsubscribe

Typical events: Add objects Set goals Set states Request shared objects

Move objects Delete objects Remove goals Remove states Release shared objects

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RMI (Remote Method Invocation)

RMI facilitates the interprocess communication between Java Virtual Machines.

RMI enables remote method invocation. RMI server creates the remote objects. RMI client invokes a method on a remote object. Example

Naming.bind(“rmi:///CalendarImpl”, new CalendarImpl()); //create an instance of object and bind it to name CalendarImpl

Naming.lookup(“rmi:///cs.gmu.edu/CalendarImpl”); //lookup the remote object at the specified URI: remote objects

//bound to the name CalendarImpl at cs.gmu.edu

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Human User & Agent Communication

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Human User & Agent Communication

The communication is enabled via Prodigy wrapper.

KQML (Knowledge Query & Manipulation Language) is used in communication. Lisp-like syntax

(tell :content (word “hello”) :sender M :receiver S)

(request :content (kill P)) (achieve :content (killed P))

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KQML - Standard Verbs

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KQML - Standard Parameters

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Control Issues (Single Plan) User sends planning request to

agent via achieve(goals) If the agent does not fully

understand the context of the goals, then it asks user via query(obj,state) such as What is initial state of the missile?

The clarification is realized by reply(obj,state)

If everything is clear then agent generates plan and send back the plan to user via tell(plan)

If there are more than one available plans in system then the agent receives a standby signal. In this case we have multiple plans

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Control Issues (Multiple Plans)

When agent receives standby signal then it asks user if s/he wants more by ask-one.

The user can cancel or next next signals agent to provide

a stream of plans. The first next provides all

needed parameters such as depth-bound, time-bound…

agent sends back first plan via tell.

The next next specifies how the next plan differs from the first (shorter, different…)

Continue until no more plan

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Control Issues (Multiple Plans)

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Shared Awareness

There are two types of communication: Among users Between user and agent.

Consequently, there are two types of shared awareness: Shared awareness among users Shared awareness between user and

agent

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Shared Awareness Among Users

There are 3 modes of planning Separate planning: Plans cannot be seen

by other planners, all resultant plans are independent.

Information sharing planning: Plans can be seen by other planners, all resultant plans are independent

Joint planning: Plans can be seen by other planners, all resultant plans combine into a complex plan.

Share/Joint planning allows shared awareness to be realized.

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Separate Planning The users do not co-

operate in separate planning.

No one can see other’s plan.

The domains from different users can be different.

In short, the two plans are total independent.

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Information Sharing Planning

The users do not co-operate in separate planning.

But one can see other’s plan.

The domains from different users can be different.

In short, the two plans are total independent.

One of purposes of this mode is to compare plans.

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Joint Planning The users do co-

operate in planning. One can see other’s

plan. The domains from

different users can be different.

In short, the two planning are independent.

Joint planning is really useful in multi-domains planning.

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Joint Planning Example of joint planning:

Suppose to send a package from a post office to an oversea country.

One planner has expertise in truck-logistics domain.

The other planner has expertise in air-logistics domain.

Neither domain is suffice to carry on the task. Neither planner can achieve the goal by

itself. Need to joint plan: from post office carry

package by truck to nearest airport. From there fly the package over to that country.

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Shared Objects Ownership of objects: in joint planning the server

serves as repository of objects. If user wants to borrow objects not owned by her/him

then s/he sends request to server. If the object is available (either not owned by any

one or owned but not in use) then that object is sharable.

Example: In military domain, an F15 is defined as being able to blow

up only 1 bridge but can damage a numbers of bridges. If the task is blowing up 2 bridges with only 1 F15 then there

are 2 solutions: Either ask for help by getting 1 more F15 if it’s available. Or reduce the goal to blow up 1 bridge and damage 1 bridge.

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Shared Awareness between user and agent

The agent and user share information during the discourse.

The agent sends to user the info based on request:All objects in domain.All available goals in

domain.All initial states in domain.All hierarchy of concepts,

predicates in domain. The information helps user

create objects, set initial states, setup goals, transform goals.

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Shared Awareness between user and agent

All objects in domain: help user select available objects in domains. Fighter jets, warships, missiles… in military domain.

Available goals in domain: help user decide what goal to achieve. Associated with bridge is goal of destroying,

building… Initial states in domain: help user setup initial

states of problem. Associated with missiles is state of being stored,

loaded… Hierarchy of concepts, predicates: help user

transform the goal. Hierarchy of predicates: is-destroyed is-damaged

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Task: User Task The planning details are

hidden from user in black-box. The user has 3 handles to the

black-box. The user controls the planning

process by:

Defining the goals. Assigning the resources. This is nice feature of Goal

Transformation. Users has more control over the process.

Set the priorities of goals (not implemented). Adjusting the goals and resources based on the world

changes as well as the response (succeeded, failed with explanations) from black-box.

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Task: Agent Task Generates the plan based on inputs

from users. Helps users create objects, setup initial

states and goals (via menus) Helps users transform the goals. There are 3 types of goal

transformation: Goal type transformation Goal argument transformation Negated goal transformation

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Goal Type Transformation Transform a goal by moving the predicate of

the goal along an abstraction hierarchy defined in the domain knowledge.

is-ineffective

is-isolated is-destroyed

(is-ineffective enemy-brigade1) (is-isolated enemy-brigade1) or (is-destroyed enemy-

brigade1) is more specific. Assume the initial plan is (is-destroyed enemy-brigade1) The discovery of non-combatants in the battle area

necessitates the change to (is-isolated enemy-brigade1)

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Goal Argument Transformation

Transform a goal by moving its arguments along the abstraction hierarchy of objects in domain knowledge. Initial goal: (destroyed-by bridge_1 F15_1) The

specific bridge bridge_1 is destroyed by specific F15_1 Transformed goal: (destroyed-by bridge F15) Any

bridge can be destroyed by any F15. The transformed goal is more generic than initial goal. When there is not enough resource to fulfill initial

goal, the transformed goal is good enough.

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Negated Goal Transformation

This type of transformation is to inverse a goal.

Purposes: for what-if scenario The user may want to see what happens if bridge_1 is

not destroyed by f15_1 Or to annul a particular goal and still want to keep

the planning process going on. Initial goal: destroy bridge_1 and bridge_2 with only 1

F15_1. => Plan failed Transformed goal: destroy bridge_2 only (more critical

infrastructure).

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Goal Satisfaction as Function of Cognitive Model

Goal satisfaction is the ratio of actual goal satisfaction achieved by the participant’s plan to the goal satisfaction achieved by the optimal plan.

Given a goal to destroy a bridge, destroying it achieves 100% goal satisfaction, damaging it achieves only 50% goal satisfaction.

The experiment uses 18 variations on the Bridge problem.

Insufficient resources exists. Uses 2 models: search and goal transformation. Search model allows partial goal planning. Goal manipulation lets users transform the goal to

achieve partial goal planning.

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Goal Satisfaction as Function of Cognitive Model

Experts are 6 out of 13 subjects who had familiarity to search in AI.

When presents with goal manipulation model, achievement is over 95 percent goal satisfaction on average.

When presents with search model, achievement is about 80 percent goal satisfaction on average.

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Goal Satisfaction as Function of Complexity

Problems with longer plan is considered more complex than problem with shorter plan.

Complexity is dependent on the number of steps in the optimal plan.

Uses the same setup as before. Categorizes 18 problems into 3 categories:

Number of steps ≤ 13: Easy 14 ≤ Number of steps ≤ 25: Medium 26 ≤ Number of steps: Hard

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Goal Satisfaction as Function of Complexity

When the goal manipulation model is presented to users, the goal satisfaction almost remains the same with increasing problem complexity.

When the search model is presented to users, the goal satisfaction decreases when the problem complexity increases.

Presents the average goal satisfaction ratio for combination of planning model and problem complexity.

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Goal Satisfaction as Function of Expertise

Using the same setup as before.

Presents the average goal satisfaction ratio for combination of planning model and expertise levels.

The experts perform better than the novices in each model.

The goal satisfaction ratio is still higher in goal manipulation model.

Both lines are not parallel and that means the difference between novices and experts in goal manipulation model is less than that of search model.

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Introduction to GTrans Example of using GTrans in planning Details of GTrans Lesson learned References

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Lesson Learned

Strength: Planning becomes easier with visualization. Hiding all search trees from novices => easier

for them to make decision. Assigning resources & transforming goals as

needed make planning more understandable. System helps users in setting up scenario and

goals as well as transforming the goal. Drag & Drop capability speeds up the planning

process.

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Lesson Learned Weakness:

All goals have the same priorities. In Joint/Share planning modes, all

information is shared. Should share only information which may interfere the planning.

Mixed-initiative is less interactive and less explicit.

No appropriate explanation for a planning failure.

No learning capability.

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Introduction to GTrans Example of using GTrans in planning Details of GTrans Lesson learned References

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References Cox, M. T., Edwin, G., Balasubramanian, K., & Elahi, M. (2001). Multiagent goal transformation and

mixed-initiative planning using Prodigy/Agent. To appear in Proceedings of the 5th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2001). http://www.cs.wright.edu/~mcox/Papers/cox-edwin-balas-elahi.ps

Cox, M., Kerkez, B., Srinivas, C., Edwin, G., Archer, W. (2000). Toward Agent-Based Mixed-Initiative Interfaces.In H. R. Arabnia (Ed.), Proceedings of the 2000 International Conference on Artificial Intelligence , Vol. 1 http://www.cs.wright.edu/people/faculty/mcox/ic-ai/abmii-camera-ready.pdf

Immaneni, T., & Cox, M. T. (2004). GTrans: An application for mixed-initiative collaborative planning during emergency response situations. In W. W. Smari & W. McQuay (Eds.), Proceedings of the 2004 International Symposium on Collaborative Technologies and Systems (CTS 04), (pp. 121-126). San Diego: Society of Modeling and Simulation International. http://www.cs.wright.edu/people/faculty/mcox/Papers/immaneni-cox-04.pdf

Veloso, M. M., Mulvehill, A. M., & Cox, M. T. (1997). Rationale-supported mixed-initiative case-based planning. In Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference (pp. 1072-1077). Menlo Park, CA: AAAI Press / The MIT Press. http://www.cs.wright.edu/~mcox/Ftp/veloso-mulvehill-cox.ps

Zhang, C., Cox, M. T., & Immaneni, T. (2002). GTrans version 2.1 User manual and reference (Tech. Rep. No. WSU-CS-02-02). Dayton, OH: Wright State University, Departmentof Computer Science and Engineering. http://www.cs.wright.edu/~mcox/Gtrans/Tech-Rep-WSU-CS-02-02.pdf

Cox, M. T. (2001). Toward tailored information presentation in support of collaborative planning. In B. Bell & E. Santos (Eds.), Intent Inference for Collaborative Tasks: Papers from the 2001 fall symposium (pp. 44-50). AAAI Technical Report FS-01-05.Menlo Park, CA :AAAI Press. http://www.cs.wright.edu/~mcox/Ftp/mcox01.pdf

Cox ,M.T.(2000).A conflict of metaphors :Modeling the planning process.In Proceedings of 2000 Summer Computer Simulation Conference (pp. 666-671) .San Diego :The Society for Computer Simulation International. http://www.cs.wright.edu/people/faculty/mcox/Ftp/metapho-camera-ready.pdf

Finin, T., McKay, D., & Fritzson, R. (1992). An Overview of KQML: A Knowledge Query and Manipulation Language. Computer Science Department, University of Maryland. http://citeseer.nj.nec.com/chalupsky92overview.html

Zhang, C. (2002), Cognitive Models For Mixed-Initiative Planning. Master’s thesis. Wright State University, Dayton, OH. http://www.cs.wright.edu/~mcox/GTrans/zhang2002.pdf

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References

Cox, M. T. (in press). Planning as mixed-initiative goal manipulation . To appear in the Proceedings of the Workshop on Mixed-Initiative Intelligent Systems at the 18th International Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press. http://www.cs.wright.edu/~mcox/Ftp/cox-ijcai03.pdf

Michael T. Cox, Thomas Hartrum, Scott DeLoach,1 and S. Narayanan. Agent-Based Mixed-Initiative Collaboration: The ABMIC project final report. Wright State University Technical Report WSU-CS-02-01, July 2002. http://www.cis.ksu.edu/~sdeloach/ai/publications/ABMIC-final.report2.pdf

Cox, M. T., & Veloso, M. M. (1997a). Controlling for unexpected goals when planning in a mixed-initiative setting. In E. Costa & A. Cardoso (Eds.), Progress in Artificial Intelligence: Eighth Portuguese Conference on Artificial Intelligence (pp. 309-318). Berlin: Springer. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/m-init3.html

Cox, M. T., & Veloso, M. M. (1997b). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 531-540). Berlin: Springer-Verlag. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/ui.ps.gz

Cox, M. T., & Veloso, M. M. (1997c). Supporting combined human and machine planning: The Prodigy 4.0 User Interface Version 2.0 (Tech, Rep. No. CMU-CS-97-174). Pittsburgh: Carnegie Mellon University, Computer Science Department. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/ui-tech-rep.ps.gz

Cox, M. T., & Veloso, M. M. (in press). Goal Transformations in Continuous Planning. In M. desJardins (Ed.), Proceedings of the 1998 AAAI Fall Symposium on Distributed Continual Planning. Menlo Park, CA: AAAI Press / The MIT Press. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/goal-trans.ps.gz

Tecuci, G. Lecture notes of CS785 George Mason University, 2001. Stanescu, B. Presentation: The Trips system. George Mason University, 2004.

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