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'''Soar'''<ref name="Laird 2012"/> is a [[cognitive architecture]],<ref name="Newell 1990"/> originally created by [[John E. Laird|John Laird]], [[Allen Newell]], and [[Paul Rosenbloom]] at [[Carnegie Mellon University]]. (Rosenbloom continued to serve as co-principal investigator after moving to [[Stanford University]], then to the [[University of Southern California]]'s Information Sciences Institute.) It is now [https://soar.eecs.umich.edu/ maintained and developed] by John Laird's research group at the [[University of Michigan]].

The goal of the Soar project is to develop the fixed computational building blocks necessary for general [[intelligent agent]]s – agents that can perform a wide range of tasks and encode, use, and learn all types of knowledge to realize the full range of cognitive capabilities found in humans, such as decision making, problem solving, planning, and natural-language understanding. It is both a theory of what [[cognition]] is and a computational implementation of that theory. Since its beginnings in 1983 as [[John E. Laird|John Laird’s]] thesis, it has been widely used by AI researchers to create intelligent agents and [[cognitive model]]s of different aspects of [[human behavior]]. The most current and comprehensive description of Soar is the 2012 book, ''The Soar Cognitive Architecture.''<ref name="Laird 2012"/>

==Theory==
Soar embodies multiple hypotheses about the computational structures underlying [[Artificial general intelligence|general intelligence]], many of which are shared with other cognitive architectures, including [[ACT-R]], which was created by [[John Robert Anderson (psychologist)|John R. Anderson]], and [[LIDA (cognitive architecture)|LIDA]], which was created by [[Stan Franklin]]. Recently, the emphasis on Soar has been on general AI (functionality and efficiency), whereas the emphasis on ACT-R has always been on [[cognitive model]]ing (detailed modeling of human cognition).

The original theory of cognition underlying Soar is the Problem Space Hypothesis, which is described in [[Allen Newell]]'s book, ''[[Unified theories of cognition|Unified Theories of Cognition]]''.<ref name="Newell 1990"/> and dates back to one of the first AI systems created, Newell, [[Herbert A. Simon|Simon]], and [[Cliff Shaw|Shaw]]'s [[Logic Theorist]], first presented in 1955. The Problem Space Hypothesis contends that all goal-oriented behavior can be cast as search through a space of possible states (a ''problem space'') while attempting to achieve a goal. At each step, a single operator is selected, and then applied to the agent’s current state, which can lead to internal changes, such as retrieval of knowledge from long-term memory or modifications or external actions in the world. (Soar’s name is derived from this basic cycle of State, Operator, And Result; however, it is no longer regarded as an acronym.) Inherent to the Problem Space Hypothesis is that all behavior, even a complex activity such as planning, is decomposable into a sequence of selection and application of primitive operators, which when mapped onto human behavior take ~50ms.

A second hypothesis of Soar’s theory is that although only a single operator can be selected at each step, forcing a serial bottleneck, the processes of selection and application are implemented through parallel rule firings, which provide context-dependent retrieval of procedural knowledge.

A third hypothesis is that if the knowledge to select or apply an operator is incomplete or uncertain, an impasse arises and the architecture automatically creates a substate. In the substate, the same process of problem solving is recursively used, but with the goal to retrieve or discover knowledge so that decision making can continue. This can lead to a stack of substates, where traditional problem methods, such as [[planning]] or [[Hierarchical task network|hierarchical task decomposition]], naturally arise. When results created in the substate resolve the impasse, the substate and its associated structures are removed. The overall approach is called Universal Subgoaling.

These assumptions lead to an architecture that supports three levels of processing. At the lowest level, is bottom-up, parallel, and automatic processing. The next level is the deliberative level, where knowledge from the first level is used to propose, select, and apply a single action. These two levels implement fast, skilled behavior, and roughly correspond to [[Daniel Kahneman|Kahneman’s]] System 1 processing level. More complex behavior arises automatically when knowledge is incomplete or uncertain, through a third level of processing using substates, roughly corresponding to System 2.

A fourth hypothesis in Soar is that the underlying structure is modular, but not in terms of task or capability based modules, such as planning or language, but instead as task independent modules including: a decision making module; memory modules (short-term spatial/visual and working memories; long-term procedural, declarative, and episodic memories), learning mechanisms associated with all long-term memories; and perceptual and motor modules. There are further assumptions about the specific properties of these memories described below, including that all learning is online and incremental.

A fifth hypothesis is that memory elements (except those in the spatial/visual memory) are represented as symbolic, relational structures.  The hypothesis that a [[symbolic artificial intelligence|symbolic system]] is necessary for general [[intelligence (trait)|intelligence]] is known as the ''[[physical symbol system]] hypothesis''. An important evolution in Soar is that all symbolic structures have associated statistical metadata (such as information on recency and frequency of use, or expected future reward) that influences retrieval, maintenance, and learning of the symbolic structures.

==Architecture==

===Processing cycle – decision procedure===
Soar’s main processing cycle arises from the interaction between [[procedural memory]] (its knowledge about how to do things) and [[working memory]] (its representation of the current situation) to support the selection and application of operators. Information in working memory is represented as a [[symbolic system|symbolic graph structure]], rooted in a ''state.'' The knowledge in procedural memory is represented as if-then [[Production (computer science)|rules]] (sets of conditions and actions), that are continually matched against the contents of working memory. When the conditions of a rule matches structures in working memory, it ''fires'' and performs its actions. This combination of rules and working memory is also called a [[Production system (computer science)|production system]]. In contrast to most production systems, in Soar, all rules that match, fire in parallel.

Instead of having the selection of a single rule being the crux of decision making, Soar’s decision making occurs through the selection and applications of ''operators'', that are proposed, evaluated, and applied by rules. An operator is proposed by rules that test the current state and create a representation of the operator in working memory as well as an ''acceptable preference'', which indicates that the operator should be considered for selection and application. Additional rules match with the proposed operator and create additional preferences that compare and evaluate it against other proposed operators. The preferences are analyzed by a decision procedure, which selects the preferred operator and installs it as the current operator in working memory. Rules that match the current operator then fire to apply it and make changes to working memory. The changes to working memory can be simple inferences, queries for retrieval from Soar’s long-term semantic or episodic memories, commands to the motor system to perform actions in an environment, or interactions with the Spatial Visual System (SVS), which is working memory’s interface to perception. These changes to working memory lead to new operators being proposed and evaluated, followed by the selection of one and its application.

===Reinforcement learning===
Soar supports [[reinforcement learning]], which tunes the values of rules that create numeric preferences for evaluating operators, based on reward. To provide maximal flexibility, there is a structure in working memory where reward is created.

===Impasses, substates, and chunking===
If the preferences for the operators are insufficient to specify the selection of a single operator, or there are insufficient rules to apply an operator, an impasse arises. In response to an impasse, a substate is created in working memory, with the goal being to resolve the impasse. Additional procedural knowledge can then propose and select operators in the substate to gain more knowledge, and either create preferences in the original state or modify that state so the impasse is resolved. Substates provide a means for on-demand complex reasoning, including hierarchical task decomposition, planning, and access to the declarative long-term memories. Once the impasse is resolved, all of the structures in the substate are removed except for any results. Soar’s chunking mechanism compiles the processing in the substate which led to results into rules. In the future, the learned rules automatically fire in similar situations so that no impasse arises, incrementally converting complex reasoning into automatic/reactive processing. Recently, the overall Universal Subgoaling procedure has been extended through a mechanism of goal-directed and automatic knowledge base augmentation that allows to solve an impasse by recombining, in an innovative and problem-oriented way, the knowledge possessed by a Soar agent.<ref name="Lieto 2019"/>

===Symbolic input and output===
Symbolic input and output occurs through working memory structures attached to the top state called the input-link and the output-link. If structures are created on the output-link in working memory, these are translated into commands for external actions (e.g., motor control).

===Spatial visual system and mental imagery===
To support interaction with vision systems and non-symbolic reasoning, Soar has its Spatial Visual System (SVS). SVS internally represents the world as a ''scene graph'', a collection of objects and component subobjects each with spatial properties such as shape, location, pose, relative position, and scale. A Soar agent using SVS can create filters to automatically extract features and relations from its scene graph, which are then added to working memory. In addition, a Soar agent can add structures to SVS and use it for mental imagery. For example, an agent can create a hypothetical object in SVS at a given location and query to see if it collides with any perceived objects.

===Semantic memory===
[[Semantic memory|Semantic Memory]] (SMEM) in Soar is designed to be a very large long-term memory of fact-like structures. Data in SMEM is represented as directed cyclic graphs. Structures can be stored or retrieved by rules that create commands in a reserved area of working memory. Retrieved structures are added to working memory.

SMEM structures have activation values that represent the frequency or recency of usage of each memory, implementing the ''base-level activation'' scheme originally developed for ACT-R. During retrieval, the structure in SMEM that matches the query and has the highest activation is retrieved. Soar also supports ''[[spreading activation]]'', where activation spreads from SMEM structures that have been retrieved into working memory to other long-term memories that they are linked to.<ref name="Jones 2016"/> These memories in turn spread activation to their neighbor memories, with some decay. Spreading activation is a mechanism for allowing the current context to influence retrievals from semantic memory.

===Episodic memory===
[[Episodic memory|Episodic Memory]] (EPMEM) automatically records snapshots of working memory in a temporal stream. Prior episodes can be retrieved into working memory through query. Once an episode has been retrieved, the next (or previous) episode can then be retrieved. An agent may employ EPMEM to sequentially play through episodes from its past (allowing it to predict the effects of actions), retrieve specific memories, or query for episodes possessing certain memory structures.

===Learning===
Each of Soar’s long-term memories have associated online learning mechanisms that create new structures or modify metadata based on an agent’s experience. For example, Soar learns new rules for procedural memory through a process called ''chunking'' and uses reinforcement learning to tune rules involved in the selection of operators.

==Agent development==
The standard approach to developing an agent in Soar starts with writing rules that are loaded into procedural memory, and initializing semantic memory with appropriate declarative knowledge.  
The process of agent development is explained in detail in the official Soar manual as well as in several tutorials which are provided at the research group's [http://soar.eecs.umich.edu/articles/articles website].

==Software==
[[File:Extending the Soar Cognitive Architecture by John Laird of University of Michigan.jpg|thumb|Extending the Soar Cognitive Architecture by John Laird, 2008.]]
The Soar architecture is maintained and extended by John Laird's research group at the University of Michigan. The current architecture is written in a combination of C and C++, and is freely available (BSD license) at the research group's [http://soar.eecs.umich.edu/ website].

Soar can interface with external language environments including C++, Java, Tcl, and Python through the Soar Markup Language (SML). SML is a primary mechanism for creating instances of Soar agents and interacting with their I/O links.

JSoar is an implementation of Soar written in Java. It is maintained by [http://soartech.com/ SoarTech], an AI research and development company. JSoar closely follows the University of Michigan architecture implementation, although it generally does not reflect the latest developments and changes of that C/C++ version.<ref name="JSoar"/>

==Applications==
Below is a historical list of different areas of applications that have been implemented in Soar. There have been over a hundred systems implemented in Soar, although the vast majority of them are toy tasks or puzzles.

===Puzzles and games===
Throughout its history, Soar has been used to implement a wide variety of classic AI puzzles and games, such as Tower of Hanoi, Water Jug, Tic Tac Toe, Eight Puzzle, Missionaries and Cannibals, and variations of the [[Blocks world]]. One of the initial achievements of Soar was showing that many different weak methods would naturally arise from the task knowledge that was encoded in it, a property called, the ''Universal Weak Method.'' <ref name="Laird 1983"/>

===Computer configuration===
The first large-scale application of Soar was R1-Soar, a partial reimplementation by Paul Rosenbloom of the R1 ([[XCON]]) [[expert system]] John McDermott developed for configuring DEC computers. R1-Soar demonstrated the ability of Soar to scale to moderate-size problems, use hierarchical task decomposition and planning, and convert deliberate planning and problem solving to reactive execution through chunking.<ref name="Rosenbloom 1985"/>

===Natural-language understanding===
NL-Soar was a [[natural-language understanding]] system developed in Soar by Jill Fain Lehman, Rick Lewis, Nancy Green, Deryle Lonsdale and Greg Nelson. It included capabilities for natural-language comprehension, generation, and dialogue, emphasizing real-time incremental parsing and generation. NL-Soar was used in an experimental version of TacAir-Soar and in NTD-Soar.<ref name="Rubinoff 1994"/>

===Simulated pilots===
The second large-scale application of Soar involved developing agents for use in training in large-scale distributed simulation. Two major systems for flying U.S. tactical air missions were co-developed at the University of Michigan and Information Sciences Institute (ISI) of University of Southern California. The Michigan system was called TacAir-Soar and flew (in simulation) [[Fixed-wing aircraft|fixed-wing]] U. S. military tactical missions (such as close-air support, strikes, [[Civil Air Patrol|CAPs]], refueling, and [[Suppression of Enemy Air Defenses|SEAD]] missions). The ISI system was called RWA-Soar and flew rotary-wing (helicopter) missions. Some of the capabilities incorporated in TacAir-Soar and RWA-Soar were attention, situational awareness and adaptation, real-time planning and dynamic replanning, and complex communication, coordination, and cooperation among combinations of Soar agents and humans. These systems participated in [[DARPA]]’s [[Synthetic natural environment|Synthetic Theater of War]] (STOW-97) Advanced Concept Technology Demonstration (ACTD), which at the time was the largest fielding of synthetic agents in a joint battlespace over a 48-hour period, and involved training of active duty personnel. These systems demonstrated the viability of using AI agents for large-scale training.<ref name="Jones 1999"/>

===STEAM===
One of the important outgrowths of the RWA-Soar project was the development of STEAM by [[Milind Tambe]],<ref name="Tambe 1997"/> a framework for flexible teamwork in which agents maintained models of their teammates using the joint intentions framework by Cohen & [[Hector Levesque|Levesque]].<ref name="Cohen 1991"/>

===NTD-Soar===
NTD-Soar was a simulation of the [[Launch Control Center|NASA Test Director]] (NTD), the person responsible for coordinating the preparation of the [[NASA]] [[Space Shuttle]] before launch. It was an integrated cognitive model that incorporated many different complex cognitive capabilities including [[natural language processing|natural-language processing]], [[attention]] and [[visual search]], and problem solving in a broad agent model.<ref name="Nelson 1994"/>

===Virtual humans===
Soar has been used to simulate virtual humans supporting face-to-face dialogues and collaboration within a virtual world developed at the Institute of Creative Technology at USC. Virtual humans have integrated capabilities of [[perception]], [[natural-language understanding]], [[emotions]], body control, and action, among others.<ref name="VanLent 2002"/>

===Game AIs and mobile apps===
Game AI agents have been built using Soar for games such as [[StarCraft: Brood War|StarCraft]],<ref name="Turner 2013"/> [[Quake II]],<ref name="Laird 2001a"/> [[Descent 3]],<ref name="vanLent 1999"/> [[Unreal Tournament]],<ref name="Wray 2002"/> and [[Minecraft]]{{Citation needed|date=January 2018}}, supporting capabilities such as [[Spatial–temporal reasoning|spatial reasoning]], [[real-time strategy]], and opponent [[Anticipation (artificial intelligence)|anticipation]]. AI agents have also been created for video games including Infinite [[Mario]]<ref name="Mohan 2009"/> which used reinforcement learning, and [[Frogger II]], [[Space Invaders]], and Fast Eddie, which used both reinforcement learning and [[mental image]]ry.<ref name="Wintermute 2010"/>

Soar can run natively on [[mobile device]]s. A mobile [https://itunes.apple.com/us/app/michigan-liars-dice/id562997948 application] for the game [[Liar's dice|Liar’s Dice]] has been developed for [[iOS]] which runs the Soar architecture directly from the phone as the engine for opponent AIs.<ref>{{Cite web|url=https://github.com/SoarGroup/Domains-DiceiOS|title=Michigan Liar's Dice|author=University of Michigan|date=19 May 2015|publisher=GitHub|access-date=21 January 2017}}</ref>

===Robotics===
Many different robotic applications have been built using Soar since the original Robo-Soar was implemented in 1991 for controlling a Puma robot arm.<ref name="Laird 2001b"/> These have ranged from mobile robot control to humanoid service [[REEM]] robots,<ref name="Puigbo 2013"/> taskable robotic mules<ref name="Taylor 2014"/> and [[unmanned underwater vehicle]]s.<ref name="NavyOffice 2014"/>

===Interactive task learning===
A current focus of research and development in the Soar community is Interactive Task Learning (ITL), the automatic learning of new tasks, environment features, behavioral constraints, and other specifications through natural instructor interaction.<ref name="Laird 2014"/> Research in ITL has been applied to tabletop game playing<ref name="Kirk 2016"/> and multi-room navigation.<ref name="Mininger 2016"/>

===Scheduling===
Early on, Merle-Soar demonstrated how Soar could learn a complex scheduling task modeled after the lead human scheduler in a windshield production plant located near Pittsburgh.<ref>{{Cite journal|last1=Prietula|first1=Michael|last2=Hsu|first2=Wen-Ling|last3=Steier|first3=David|last4=Newell|date=1993|editor4-first=Allen|title=Applying an architecture for general intelligence to reduce scheduling effort|journal=ORSA Journal on Computing|volume=5|issue=3|pages=304–320|doi=10.1287/ijoc.5.3.304|s2cid=6813564 }}</ref> Later, a generalized version of Merle-Soar (Dispatcher-Soar) was used to demonstrate a symbolic, constraint propagation approach in learning to improve schedules and to define task-independent knowledge metrics of architecture-specific learning -- knowledge efficiency, knowledge utility, and knowledge effectiveness.<ref>{{Cite journal |last=Zhu |first=D. |last2=Prietula |first2=M. |last3=Hsu |first3=W-L. |date=1997 |title=When Processes Learn: Steps Toward Crafting an Intelligent Organization |url=https://doi.org/10.1287/isre.8.3.302 |journal=Information Systems Research |volume=8 |issue=3 |pages=302-317 |via=}}</ref>

=== Music ===
Melody-Soar demonstrated how the Soar architecture could explain and demonstrate creativity in simple melody generation using hierarchies of problems spaces that parallel the hierarchical structure of melody, allowing unique melodies to be generated from preferences of existing styles (e.g., Bach).<ref>{{Cite book |last=Vicinanza |first=S. |title=The Soar Papers: Research on Integrated Intelligence |last2=Prietula |first2=M. |publisher=MIT Press |editor-last=Rosenbloom |editor-first=P. |volume=2 |publication-place=Cambridge, MA |chapter=Chapter 45: A Computational Model of Musical Creativity |editor-last2=Laird |editor-first2=J. |editor-last3=Newell |editor-first3=A.}}</ref>
==See also==
* [[Cognitive architecture|Cognitive Architecture]] 
* [[ACT-R]]

==References==
{{Reflist |refs=
<ref name="Laird 2012">{{cite book|last=Laird|first=John E.|date=2012|url=https://mitpress.mit.edu/books/soar-cognitive-architecture|title=The Soar Cognitive Architecture |publisher=[[MIT Press]] |isbn= 978-0262122962}}</ref>

<ref name="Laird 1983">{{cite journal|last1=Laird|first1=John|last2=Newell|first2=Allen|date=1983|title= A Universal Weak Method: Summary of results|journal=IJCAI|volume=2|pages=771–772|url=http://dl.acm.org/citation.cfm?id=1623558}}</ref>

<ref name="Rosenbloom 1985">{{cite journal|last1=Rosenbloom|first1=Paul|last2=Laird|first2=John|last3=Mcdermott|first3=John|title=R1-Soar: An Experiment in Knowledge-Intensive Programming in a Problem-Solving Architecture|journal=IEEE Transactions on Pattern Analysis and Machine Intelligence|date=27 January 2009|volume=PAMI-7|issue=5|pages=561–569|doi=10.1109/TPAMI.1985.4767703|pmid=21869293|s2cid=16133794}}</ref>

<ref name="Newell 1990">{{cite book |last=Newell |first=Allen |date=December 1990 |title=Unified Theories of Cognition |publisher=Harvard University Press |isbn=978-0674920996 |url-access=registration |url=https://archive.org/details/unifiedtheorieso0000newe }}</ref>

<ref name="Cohen 1991">{{cite journal|last1=Cohen|first1=Philip|last2=Levesque|first2=Hector|title=Confirmations and joint action|journal=IJCAI|date=1991|volume=2|pages=951–957|url=http://dl.acm.org/citation.cfm?id=1631603}}</ref>

<ref name="Rubinoff 1994">{{cite journal|last1=Rubinoff|first1=Robert|last2=Lehman|first2=Jill|title=Real-time natural language generation in NL-Soar|journal=INLG|volume=Proceedings of the Seventh International Workshop on Natural Language Generation|date=1994|pages=199–206|doi=10.3115/1641417.1641440|s2cid=13885938|url=http://dl.acm.org/citation.cfm?id=1641440|doi-access=free}}</ref>

<ref name="Nelson 1994">{{cite journal|last1=Nelson|first1=G|last2=Lehman|first2=J|last3=John|first3=B|title=Integrating cognitive capabilities in a real-time task|journal=Proceedings of the 16th Annual Conference of the Cognitive Science Society|date=1994|pages=658–663|url=https://www.bibsonomy.org/bibtex/d8033d7496b2f034268d170b45686fb7}}</ref>

<ref name="Tambe 1997">{{cite journal|last1=Tambe|first1=Milind|title=Agent Architectures for Flexible, Practical Teamwork|journal=AAAI|volume=Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence|date=1997|pages=22–28|url=http://dl.acm.org/citation.cfm?id=1867410}}</ref>

<ref name="vanLent 1999">{{cite journal|last1=van Lent|first1=Michael|last2=Laird|first2=John|title=Developing an artificial intelligence engine|date=1991|url=https://www.researchgate.net/publication/243763189}}</ref>

<ref name="Laird 2001a">{{cite book|last1=Laird|first1=John|title=It Knows What You'Re Going to Do: Adding Anticipation to a Quakebot|journal=Agents|date=2001|volume=Proceedings of the Fifth International Conference on Autonomous Agents|pages=385–392|doi=10.1145/375735.376343|isbn=978-1581133264|s2cid=3509100}}</ref>

<ref name="Laird 2001b">{{cite journal|last1=Laird|first1=John|last2=Yager|first2=Eric|last3=Hucka|first3=Michael|last4=Tuck|first4=Christopher|title=Robo-Soar: An integration of external interaction, planning, and learning using Soar|journal=Robotics and Autonomous Systems|date=November 1991|volume=8|issue=1–2|pages=113–129|doi=10.1016/0921-8890(91)90017-f|citeseerx=10.1.1.726.7247|hdl=2027.42/29045}}</ref>

<ref name="Lieto 2019">{{cite journal|last1=Lieto|first1=Antonio|last2=Perrone|first2=Federico|last3=Pozzato|first3=Gian Luca|last4=Chiodino|first4=Eleonora|date=2019|title= Beyond Subgoaling: A Dynamic Knowledge Generation Framework for Creative Problem Solving in Cognitive Architectures|journal=Cognitive Systems Research|volume=58|pages=305–316|doi=10.1016/j.cogsys.2019.08.005|hdl=2318/1726157|s2cid=201127492|hdl-access=free}}</ref>

<ref name="Wray 2002">{{cite journal|last1=Wray|first1=Robert|display-authors=etal|title=Intelligent opponents for virtual reality trainers|journal=I/Itsec|date=December 2002|volume=Proceedings of the Interservice/Industry Training, Simulation and Education Conference|citeseerx=10.1.1.549.2187}}</ref>

<ref name="Mohan 2009">{{cite journal|last1=Mohan|first1=Shiwali|last2=Laird|first2=John|title=Learning to Play Mario|journal=Technical Report|date=2009|volume=CCA-TR-2009-03|citeseerx=10.1.1.387.5972}}</ref>

<ref name="Wintermute 2010">{{cite journal|last1=Wintermute|title=Imagery in Cognitive Architecture: Representation and Control at Multiple Levels of Abstraction|journal=Cognitive Systems Research|date=September 2012|volume=19-20|pages=1–29|doi=10.1016/j.cogsys.2012.02.001|url=http://dl.acm.org/citation.cfm?id=2619015|citeseerx=10.1.1.387.5894|s2cid=15399199}}</ref>

<ref name="Turner 2013">{{cite journal|last1=Turner|first1=Alex|title=Soar-SC: A Platform for AI Research in StarCraft|date=2013|url=https://github.com/bluechill/Soar-SC}}</ref>

<ref name="NavyOffice 2014">{{cite journal|date=February 2013|journal=Office of Naval Research|volume=11|title=The Mystery of Artificial Intelligence|url=https://www.onr.navy.mil/Science-Technology/Directorates/office-innovation/~/media/Files/03I/DoI-News-FEB2014-Vol11pdf}}</ref>

<ref name="Laird 2014">{{cite journal|date=2014|last1=Laird|first1=John|title=NSF Report: Interactive Task Learning|url=http://web.eecs.umich.edu/~soar/sitemaker/docs/pubs/ITL_Report_NSF_1419590.pdf}}</ref>

<ref name="Kirk 2016">{{cite journal|last1=Kirk|first1=James|last2=Laird|first2=John|title=Learning General and Efficient Representations of Novel Games Through Interactive Instruction|journal=Advanced Cognitive Systems|date=2016|volume=4|url=http://www.cogsys.org/papers/ACS2016/Papers/Kirk_Laird-ACS-2016.pdf}}</ref>

<ref name="Mininger 2016">{{cite journal|last1=Mininger|first1=Aaron|last2=Laird|first2=John|title=Interactively Learning Strategies for Handling References to Unseen or Unknown Objects|journal=Advanced Cognitive Systems|date=2016|url=http://www.cogsys.org/papers/ACS2016/Papers/Mininger_Laird-ACS-2016.pdf}}</ref>

<ref name="VanLent 2002">{{cite journal|date=2001|last1=van Lent|first1=Mike|display-authors=etal|url=http://web.eecs.umich.edu/~soar/sitemaker/workshop/22/vanLentMRE-S22.PDF|title=ICT Mission Rehearsal Exercise}}</ref>

<ref name="Jones 1999">{{cite journal|last1=Jones|display-authors=etal|title=Automated Intelligent Pilots for Combat Flight Simulation|journal=AAAI|date=1999|volume=20|issue=1|url=http://www.aaai.org/ojs/index.php/aimagazine/article/view/1438|access-date=2017-01-21|archive-date=2018-07-28|archive-url=https://web.archive.org/web/20180728035635/https://www.aaai.org/ojs/index.php/aimagazine/article/view/1438|url-status=dead}}</ref>

<ref name="Puigbo 2013">{{cite journal|last1=Puigbo|first1=Jordi-Ysard|display-authors=etal|title=Controlling a General Purpose Service Robot By Means Of a Cognitive Architecture|journal=AIC|date=2013|volume=45|citeseerx=10.1.1.402.5541}}</ref>

<ref name="Taylor 2014">{{cite journal|last1=Talor|first1=Glen|display-authors=etal|title=Multi-Modal Interaction for Robotic Mules|journal=Soar Technology Inc.|date=February 2014}}</ref>

<ref name="Jones 2016">{{cite journal|last1=Jones|first1=Steven|display-authors=etal|title=Efficient Computation of Spreading Activation Using Lazy Evaluation|journal=ICCM|date=2016|volume=Proceedings of the 14th International Conference on Cognitive Modeling|pages=182–187|url=http://acs.ist.psu.edu/iccm2016/proceedings/jones2016iccm.pdf}}</ref>

<ref name="JSoar">[https://github.com/soartech/jsoar ''SoarTech: JSoar'']</ref>

}}

==Bibliography==
* Laird, 2012 [https://mitpress.mit.edu/books/soar-cognitive-architecture The Soar Cognitive Architecture]
* Lehman, Laird, and Rosenbloom, 2006 [https://web.eecs.umich.edu/~soar/sitemaker/docs/misc/GentleIntroduction-2006.pdf A Gentle Introduction to Soar: 2006 update]
* Rosenbloom, Laird, and Newell, 1993 [http://www.isi.edu/soar/papers/soar-papers-book/soar-papers.html The Soar Papers: Readings on Integrated Intelligence] {{Webarchive|url=https://web.archive.org/web/20160923193018/http://www.isi.edu/soar/papers/soar-papers-book/soar-papers.html |date=2016-09-23 }}, [[Information Sciences Institute]]

==External links==
*[http://soar.eecs.umich.edu/ Soar Homepage] on [[University of Michigan]]
*[http://acs.ist.psu.edu/projects/soar-faq/soar-faq.html Soar: Frequently Asked Questions List]
*[http://soartech.com/ Soar Tech Homepage]
*[https://ict.usc.edu/profile/paul-rosenbloom/ Paul Rosenbloom]

[[Category:Agent-based programming languages]]
[[Category:Cognitive architecture]]