Teamwork is best achieved when members of the team understand one another. Human–robot collaboration poses a particular challenge to this goal due to the differences between individual team members, both mentally/computationally and physically. One way in which this challenge can be addressed is by developing explicit models of human teammates. Here, we discuss, compare and contrast the many techniques available for modeling human cognition and behavior, and evaluate their benefits and drawbacks in the context of human–robot collaboration.
AarnoDEkvallSKragićD (2005) Adaptive virtual fixtures for machine-assisted teleoperation tasks. In: Proceedings of the international conference on robotics and automation.
2.
AdmoniHScassellatiB (2014) Data-driven model of nonverbal behavior for socially assistive human–robot interactions. In: Proceedings of the 16th International conference on multimodal interaction, Istanbul, Turkey, 12–16 November 2014, pp.196–199. ACM.
3.
AlbaneseMChellappaRMoscatoV. (2008) A constrained probabilistic Petri net framework for human activity detection in video. IEEE Transactions on Multimedia10(6): 982–996.
4.
AndersonJ (1983) A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior22(3): 261–295.
5.
AndersonJ (2007) How Can the Human Mind Occur in the Physical Universe?Oxford University Press.
6.
AndersonJBothellDLebiereC. (1998) An integrated theory of list memory. Journal of Memory and Language38(4): 341–380.
7.
AndristSMutluBTapusA (2015) Look like me: Matching robot personality via gaze to increase motivation. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, Seoul, Republic of Korea, pp.3603–3612. ACM.
8.
Avrahami-ZilberbrandDKaminkaGZarosimH (2005) Fast and complete symbolic plan recognition: Allowing for duration, interleaved execution, and lossy observations. In: Proceedings of the IJCAI workshop on modeling others from observations. Edinburgh, Scotland.
9.
BakerCSaxeRTenenbaumJB (2005) Bayesian models of human action understanding. In: Advances in Neural Information Processing Systems. pp.99–106.
10.
BakerCLSaxeRRTenenbaumJB (2011) Bayesian theory of mind: Modeling joint belief–desire attribution. In: Proceedings of the thirty-third annual meeting of the cognitive science society. pp. 2469–2474.
11.
BaumLE (1972) An equality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequalities3: 1–8.
12.
BennewitzMBurgardWCielniakG. (2005) Learning motion patterns of people for compliant robot motion. The International Journal of Robotics Research24(1): 31–48.
13.
BergmannKKoppS (2010) Modeling the production of co-verbal iconic gestures by learning Bayesian decision networks. Applied Artificial Intelligence24(6): 530–551.
14.
BillardASiegwartR (2004) Robot learning from demonstration. Robotics and Autonomous Systems47(2): 65–67.
15.
BlaylockNAllenJ (2014) Hierarchical goal recognition. In:
SukthankarGGoldmanRPGeibC (eds) Plan, Activity, And Intent Recognition: Theory and Practice. Morgan Kaufman. pp. 3–32.
16.
BleiDMNgAYJordanMI (2003) Latent Dirichlet allocation. The Journal of Machine Learning Research3: 993–1022.
BreazealC (2004) Social interactions in HRI: The robot view. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews34(2): 181–186.
19.
BreazealCGrayJBerlinM (2009) An embodied cognition approach to mindreading skills for socially intelligent robots. International Journal of Robotics Research28(5): 656–680.
20.
BucciarelliMMackiewiczRKhemlaniSSJohnson-LairdPN (2016) Children’s creation of algorithms: Simulations and gestures. Journal of Cognitive Psychology28(3): 1–22.
21.
BuiHH (2003) A general model for online probabilistic plan recognition. In: Proceedings of the international joint conference on artificial intelligence (IJCAI), Acapulco, pp.1309–1315.
22.
BuiHHPhungDQVenkateshS (2004) Hierarchical hidden Markov models with general state hierarchy. In: Proceedings of the national conference on artificial intelligence, 2004, pp.324–329.
23.
BuiHHVenkateshSWestG (2002) Policy recognition in the abstract hidden Markov model. Journal of Artificial Intelligence Research17: 451–499.
24.
CalinonSGuenterFBillardA (2007) On learning, representing, and generalizing a task in a humanoid robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics37(2): 286–298.
25.
CarberryS (2001) Techniques for plan recognition. User Modeling and User-Adapted Interaction11: 31–48.
26.
CastelCChaudronLTessierC (1996) What is going on? A high level interpretation of sequences of images. In: ECCV workshop on conceptual descriptions from images, pp.13–27.
27.
CastellanoGLeiteIPereiraA. (2012) Detecting engagement in HRI: An exploration of social and task-based context. In: Proceedings of the 2012 international conference on Privacy, Security, Risk and Trust (PASSAT) and 2012 international conference on social computing (SocialCom), pp.421–428. IEEE.
28.
CharniakEGoldmanRP (1993) A Bayesian model of plan recognition. Artificial Intelligence64(1): 53–79.
29.
ChoiJKimKE (2011) Inverse reinforcement learning in partially observable environments. Journal of Machine Learning Research12(Mar): 691–730.
30.
CraikK (1943) The Nature of Explanation. Cambridge University Press.
31.
DagumPGalperAHorvitzE (1992) Dynamic network models for forecasting. In: Proceedings of the eighth international conference on uncertainty in artificial intelligence, pp.41–48. Morgan Kaufmann Publishers.
32.
DaiWYangQXueGR. (2007) Boosting for transfer learning. In: Proceedings of the 24th international conference on machine learning, Corvallis, Oregon, pp.193–200. ACM.
33.
De la HigueraC (2000) Current trends in grammatical inference. In: Advances in Pattern Recognition. Springer, pp.28–31.
34.
DoucetADe FreitasNMurphyK. (2000) Rao–Blackwellised particle filtering for dynamic Bayesian networks. In: Proceedings of the sixteenth conference on uncertainty in artificial intelligence, pp.176–183. Morgan Kaufmann Publishers.
35.
DraganADSrinivasaSS (2012) Formalizing assistive teleoperation. In: Proceedings of robotics: Science and systems. Sydney, Australia, pp. 73–80.
36.
DraganADLeeKCSrinivasaSS (2013) Legibility and predictability of robot motion. In: 2013 8th ACM/IEEE international conference on human–robot interaction (HRI). pp. 301–308.
37.
EarleyJ (1970) An efficient context-free parsing algorithm. Communications of the ACM13(2): 94–102.
38.
EddySR (2004) What is a hidden Markov model?Nature Biotechnology22: pp.1315–1316.
39.
ErmesMParkkaJMantyjarviJ. (2008) Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine12(1): 20–26.
40.
FaggAHRosensteinMPlattRJr. (2004) Extracting user intent in mixed initiative teleoperator control. In: American Institute of Aeronautics and Astronautics (AIAA) 1st Intelligent systems technical conference.
41.
FongTNourbakhshIDautenhahnK (2003) A survey of socially interactive robots. Robotics and Autonomous Systems42(3): 143–166.
42.
FongTThropeCBaurC (2001) Collaboration, dialogue, and human–robot interaction. In: Proceedings of the international symposium on robotics research, pp. 255–266.
43.
ForbusKDGentnerDLawK (1995) MAC/FAC: A model of similarity-based retrieval. Cognitive Science19: 141–205.
44.
FreedmanRGJungHTZilbersteinS (2015) Temporal and object relations in unsupervised plan and activity recognition. In: AAAI fall symposium on AI for human–robot interaction (AI-HRI).
45.
FreitagDMcCallumA (2000) Information extraction with HMM structures learned by stochastic optimization. AAAI/IAAI: 584–589.
46.
GeibC (2012) Considering state in plan recognition with lexicalized grammars. In: Workshops at the twenty-sixth AAAI conference on artificial intelligence.
47.
GentnerD (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science7: 155–170.
48.
GhahramaniZ (2001) An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence15(1): 9–42.
49.
GhanemNDeMenthonDDoermannD. (2003) Representation and recognition of events in surveillance video using Petri nets. In: Computer vision and pattern recognition workshops (CVPRW).
50.
GillespieKMolineauxMFloydMW. (2015) Goal reasoning for an autonomous squad member. In: Annual conference on advances in cognitive systems: Workshop on goal reasoning.
51.
GongSXiangT (2003) Recognition of group activities using dynamic probabilistic networks. In: Proceedings of the 9th IEEE international conference on computer vision, pp.742–749.
52.
GriffithsTSteyversMBleiD.(2004) Integrating topics and syntax. In: Conference on neural information processing systems (NIPS), pp.537–544.
53.
GriffithsTLChaterNKempC. (2010) Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences14(8): 357–364.
54.
GroszBJKrausS (1999) The evolution of sharedplans. In:
WooldridgeMRaoA (eds) Foundations and Theories of Rational Agents, pp. 227–262. Springer.
55.
HechtFAzadPDillmannR (2009) Markerless human motion tracking with a flexible model and appearance learning. In: Proceedings of the 2009 IEEE international conference on robotics and automation (ICRA ’09), pp.3173–3179.
56.
HiattLMHarrisonAMTraftonJG (2011) Accommodating human variability in human–robot teams through theory of mind. In: Proceedings of the international joint conference on artificial intelligence, pp. 2066–2071.
57.
HiattLMTraftonJG (2010) A cognitive model of theory of mind. In: Proceedings of the international conference on cognitive modeling, pp. 91–96.
HostetterABAlibaliMW (2010) Language, gesture, action! A test of the gesture as simulated action framework. Journal of Memory and Language63(2): 245–257.
60.
HuynhTFritzMSchieleB (2008) Discovery of activity patterns using topic models. In: Proceedings of the 10th international conference on ubiquitous computing, pp.10–19. ACM.
61.
IbaSWegheJMVParedisCJJ. (1999) An architecture for gesture-based control of mobile robots. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS).
62.
IvanovYABobickAF (2000) Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence22(8): 852–872.
63.
JenkinsOCGonzálezGLoperMM (2007) Tracking human motion and actions for interactive robots. In: Proceedings of the ACM/IEEE international conference on human–robot interaction, pp.365–372. ACM.
64.
Johnson-LairdPN (2006) How We Reason. Oxford University Press, USA.
65.
KachergisGWyatteDO’ReillyRC. (2014) A continuous-time neural model for sequential action. Philosophical Transactions of the Royal Society B: Biological Sciences369(1655): 20130623.
66.
KahnemanD (2011) Thinking, Fast and Slow. Macmillan.
67.
KarpathyATodericiGShettyS. (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). pp. 1725–1732.
68.
KautzHAllenJ (1986) Generalized plan recognition. In: Proceedings of the fifth national conference on artificial intelligence, pp.32–37.
69.
KelleyRTavakkoliAKingC. (2008) Understanding human intentions via hidden Markov models in autonomous mobile robots. In: Proceedings of the 3rd ACM/IEEE international conference on human–robot interaction, pp.367–374. ACM.
70.
KennedyWGTraftonJG (2007) Using simulations to model shared mental models. In: Proceedings of the 8th international conference on cognitive modeling, pp.253–254.
71.
KhemlaniS (2016) Automating human inference. In: Proceedings of the 2nd IJCAI workshop on bridging the gap between human and automated reasoning, pp.1–4.
72.
KhemlaniSJohnson-LairdPN (2013) The processes of inference. Argument & Computation4(1): 4–20.
73.
KhemlaniSJohnson-LairdPN (2016) How people differ in syllogistic reasoning. In: PapafragouAGrodnerDMirmanD. (eds.) Proceedings of the 38th annual conference of the cognitive science society, pp. 2165–2170.
74.
KhemlaniSLotsteinMTraftonJG. (2015) Immediate inferences from quantified assertions. The Quarterly Journal of Experimental Psychology68(10): 2073–2096.
75.
KitaniKMSatoYSugimotoA (2007) Recovering the basic structure of human activities from a video-based symbol string. In:Proceedings of the 2007 IEEE workshop on motion and video computing (WMVC’07), p.9. IEEE.
76.
KrizhevskyASutskeverIHintonGE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. pp.1097–1105.
77.
KuehneHArslanASerreT (2014) The language of actions: Recovering the syntax and semantics of goal-directed human activities. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.780–787.
78.
KulićDTakanoWNakamuraY (2007) Representability of human motions by factorial hidden Markov models. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), pp.2388–2393. IEEE.
79.
LaveeGRudzskyMRivlinE. (2010) Video event modeling and recognition in generalized stochastic Petri nets. IEEE Transactions on Circuits and Systems for Video Technology20(1): 102–118.
80.
LeshNRichCSidnerCL (1999) Using plan recognition in human–computer collaboration. In: Proceedings of the seventh international conference on user modeling. pp. 23–32. Springer.
81.
LevineSWilliamsB (2014) Concurrent plan recognition and execution for human–robot teams. In: Proceedings of the international conference on automated planning and scheduling.
82.
LiMOkamuraAM (2003) Recognition of operator motions for real-time assistance using virtual fixtures. In:Proceedings of the 11th symposium on haptic interfaces for virtual environment and teleoperator systems, pp. 125–131.
83.
LiangCForbusK (2014) Constructing hierarchical concepts via analogical generalization. In: Proceedings of the cognitive science society.
84.
LiuCConnKSarkarN. (2008) Online affect detection and robot behavior adaptation for intervention of children with autism. IEEE Transactions on Robotics24(4): 883–896.
85.
LovettASagiEGentnerD. (2009) Modeling perceptual similarity as analogy resolves the paradox of difference detection. In: Proceedings of the 2nd international analogy conference.
86.
MakinoTTakeuchiJ (2012) Apprenticeship learning for model parameters of partially observable environments. In: Proceedings of the 29th international conference on machine learning.
87.
MarrD (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: W. H. Freeman.
88.
MaynordMVattamSAhaDW (2015) Increasing the runtime speed of case-based plan recognition. In: Proceedings of the twenty-eighth florida artificial intelligence research society conference, pp. 385–390.
89.
MinnenDEssaIStarnerT (2003) Expectation grammars: Leveraging high-level expectations for activity recognition. In: Proceedings. 2003 IEEE computer society conference on computer vision and pattern recognition, volume 2, pp.II–626. IEEE.
90.
MooreDEssaI (2002) Recognizing multitasked activities from video using stochastic context-free grammar. In: AAAI/IAAI, pp.770–776.
91.
Morales SaikiLYSatakeSHuqR. (2012) How do people walk side-by-side? Using a computational model of human behavior for a social robot. In: Proceedings of the seventh annual ACM/IEEE international conference on human–robot interaction, pp.301–308. ACM.
92.
MowerEFeil-SeiferDJMataricMJ. (2007) Investigating implicit cues for user state estimation in human–robot interaction using physiological measurements. In: Proceedings of the 16th IEEE international symposium on robot and human interactive communication (RO-MAN 2007), pp.1125–1130. IEEE.
93.
MurataM (1989) Petri nets: Properties, analysis and applications. Proceedings of the IEEE77(4): 541–580.
94.
MurphyKP (2002) Dynamic Bayesian networks: Representation, inference and learning. PhD Thesis, University of California, Berkeley, USA.
95.
NatarajanPNevatiaR (2007) Coupled hidden semi Markov models for activity recognition. In: IEEE workshop on motion and video computing. IEEE.
96.
NevatiaRZhaoTHongengS (2003) Hierarchical language-based representation of events in video streams. In: Proceedings of the 2003 computer vision and pattern recognition workshop (CVPRW’03), volume 4, p.39. IEEE.
97.
NguyenNTPhungDQVenkateshS. (2005) Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In: Proceedings of the computer vision and pattern recognition workshop (CVPR’05), pp.955–960. IEEE.
98.
NikolaidisSGuKRamakrishnanR. (2014) Efficient model learning for human–robot collaborative tasks. arXiv Preprint arXiv:1405.6341.
99.
OliverNHorvitzE (2005) A comparison of HMMs and dynamic Bayesian networks for recognizing office activities. In: Proceedings of the international conference on User Modeling, pp. 199–209.
100.
OliverNHorvitzEGargA (2002) Layered representations for human activity recognition. In: Proceedings of the fourth IEEE international conference on multimodal interfaces.
101.
Ou-YangCWinarjoH (2011) Petri-net integration: An approach to support multi-agent process mining. Expert Systems with Applications38(4): 4039–4051.
102.
PavlovićVRehgJMChamTJ. (1999) A dynamic Bayesian network approach to figure tracking using learned dynamic models. In: Proceedings of the seventh IEEE international conference on computer vision, pp.94–101.
103.
Pérez-D’ArpinoCShahJA (2015) Fast target prediction of human reaching motion for cooperative human–robot manipulation tasks using time series classification. In: Proceedings of the IEEE international conference on robotics and automation (ICRA).
104.
PerraultRAllenJ (1980) A plan-based analysis of indirect speech acts. American Journal of Computational Linguistics6(3-4): 167–182.
105.
PynadathDVWellmanMP (2000) Probabilistic state-dependent grammars for plan recognition. In: Proceedings of the sixteenth conference on uncertainty in artificial intelligence, pp.507–514. Morgan Kaufmann Publishers.
106.
RabinerLR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE77(2): 257–286.
107.
RamırezMGeffnerH (2011) Goal recognition over POMDPS: Inferring the intention of a POMDP agent. In: International joint conference on artificial intelligence, pp.2009–2014.
108.
RaviNDandekarNMysoreP. (2005) Activity recognition from accelerometer data. In: Association for the advancement of artificial intelligence (AAAI).
109.
ReasonJT (1984) Absent-mindedness and cognitive control. In: HarrisJMorrisP (eds) Everyday Memory, Actions and Absent-Mindedness. London, Academic Press.
110.
RiepingKEnglebienneGKröseB (2014) Behavior analysis of elderly using topic models. Pervasive and Mobile Computing15: 181–199.
111.
RisticBArulampalamSGordonN (2004) Beyond the Kalman filter. IEEE Aerospace and Electronic Systems Magazine19(7): 37–38.
112.
RotaNThonnatM (2000) Activity recognition from video sequences using declarative models. In: European conference on artificial intelligence (ECAI), pp.673–680.
113.
RyooMSAggarwalJK (2006) Recognition of composite human activities through context-free grammar based representation. In: 2006 IEEE computer society conference on computer vision and pattern recognition, volume 2, pp.1709–1718. IEEE.
114.
RyooMSAggarwalJK (2009) Semantic representation and recognition of continued and recursive human activities. International Journal of Computer Vision82(1): 1–24.
115.
ScassellatiB (2002) Theory of mind for a humanoid robot. Autonomous Robots12(1): 13–24.
116.
SchmidhuberJ (2015) Deep learning in neural networks: An overview. Neural Networks61: 85–117.
117.
SchneiderDAndersonJ (2011) A memory-based model of Hick’s law. Cognitive Psychology62(3): 193–222.
118.
StewartTCEliasmithC (2008) Building production systems with realistic spiking neurons. In: Proceedings of the annual meeting of the cognitive science society.
119.
StolckeAOmohundroS (1993) Hidden Markov model induction by Bayesian model merging.In: Advances in Neural Information Processing Systems, pp. 11–18.
120.
StoutRJCannon-BowersJASalasE. (1999) Planning, shared mental models, and coordinated performance: An empirical link is established. Human Factors: The Journal of the Human Factors and Ergonomics Society41(1): 61–71.
121.
TangY (2013) Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239.
TenenbaumJB (1999) Bayesian modeling of human concept learning. In: Advances in Neural Information Processing Systems.
124.
ToshevASzegedyC (2014) DeepPose: Human pose estimation via deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 1653–1660.
125.
TraftonJGAltmannEMRatwaniRM (2011) A memory for goals model of sequence errors. Cognitive Systems Research12: 134–143.
126.
TraftonJGHiattLMHarrisonAM. (2013) ACT-R/E: An embodied cognitive architecture for human–robot interaction. Journal of Human–Robot Interaction2(1): 30–55.
127.
TraftonJGJacobsAHarrisonAM (2012) Building and verifying a predictive model of interruption resumption. Proceedings of the IEEE100(3): 648–659.
128.
TuragaPChellappaRSubrahmanianVS. (2008) Machine recognition of human activities: A survey. IEEE transactions on circuits and systems for video technology18(11): 1473–1488.
129.
VasquezDFraichardTAycardO. (2008) Intentional motion on-line learning and prediction. Machine Vision and Applications19(5-6): 411–425.
130.
VilainMB (1990) Getting serious about parsing plans: A grammatical analysis of plan recognition. In: Conference on artificial intelligence (AAAI), pp.190–197.
131.
VinokurovYLebiereCWyatteD. (2012) Unsurpervised learning in hybrid cognitive architectures. In: Neural-symbolic learning and reasoning workshop at AAAI.
132.
VuVTBremondFThonnatM (2003) Automatic video interpretation: A novel algorithm for temporal scenario recognition. In: International joint conference on artificial intelligence (IJCAI), volume 3, pp.1295–1300.
133.
WagnerSMNusbaumHGoldin-MeadowS (2004) Probing the mental representation of gesture: Is handwaving spatial?Journal of Memory and Language50(4): 395–407.
134.
WangSYangJChenN. (2005) Human activity recognition with user-free accelerometers in the sensor networks. In: Proceedings of the international conference on neural networks and brain (ICNNB).
135.
WangYMoriG (2009) Human action recognition by semi-latent topic models. IEEE transactions on pattern analysis and machine intelligence31(10): 1762–1774.
136.
WhiteBABlaylockNBölöniL (2009) Analyzing team actions with cascading HMM. In: FLAIRS conference.
137.
WilenskyR (1983) Planning and Understanding. Addison-Wesley.
138.
WonKJPrügel-BennettAKroghA (2004) Training HMM structure with genetic algorithm for biological sequence analysis. Bioinformatics20(18): 3613–3619.
139.
YinJMengY (2010) Human activity recognition in video using a hierarchical probabilistic latent model. In: Proceedings of the computer vision and pattern recognition workshops (CVPRW), pp.15–20.
140.
YuWAlqasemiRDubeyR. (2005) Telemanipulation assistance based on motion intention recognition. In: Proceedings of the international conference on robotics and automation (ICRA), pp. 1121–1126.
141.
ZhangHParkerLE (2011) 4-dimensional local spatio-temporal features for human activity recognition. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), pp.2044–2049.
