Skip to main content
Springer Nature Link
Log in

Observer effect from stateful resources in agent sensing

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

In many real-world applications of multi-agent systems, agent reasoning suffers from bounded rationality caused by both limited resources and limited knowledge. When agent sensing to overcome its knowledge limitations also requires resource use, the agent’s knowledge refinement is affected due to its inability to always sense when and as accurately as needed, further leading to poor decision making. In this paper, we consider what happens when sensing actions require the use of stateful resources, which we define as resources whose state-dependent behavior changes over time based on usage. Current literature addressing agent sensing with limited resources primarily investigates stateless resources, such as avoiding the use of too much time or energy during sensing. However, sensing itself can change the state of a resource, and thus its behavior, which affects both the information gathered and the resulting knowledge refinement. This produces a phenomenon where the sensing action can and will distort its own outcome (and potentially future outcomes), termed the Observer Effect (OE) after the similar phenomenon in the physical sciences. Under this effect, when deliberating about when and how to perform sensing that requires use of stateful resources, an agent faces a strategic tradeoff between satisfying the need for (1) knowledge refinement to support its reasoning, and (2) avoiding knowledge corruption due to distorted sensing outcomes. To address this tradeoff, we model sensing action selection as a partially observable Markov decision process where an agent optimizes knowledge refinement while considering the (possibly hidden) state of the resources used during sensing. In this model, the agent uses reinforcement learning to learn a controller for action selection, as well as how to predict expected knowledge refinement based on resource use during sensing. Our approach is unique from other bounded rationality and sensing research as we consider how to make decisions about sensing with stateful resources that produce side effects such as the OE, as opposed to simply using stateless resources with no such side effect. We evaluate our approach in a fully and partially observable agent mining simulation. The results demonstrate that considering resource state and the OE during sensing action selection through our approach (1) yielded better knowledge refinement, (2) appropriately balanced current and future refinement to avoid knowledge corruption, and (3) exploited the relationship (i.e., high, positive correlation) between sensing and task performance to boost task performance through improved sensing. Further, our methodology also achieved good knowledge refinement even when the OE is not present, indicating that it can improve sensing performance in a wide variety of environments. Finally, our results also provide insights into the types and configurations of learning algorithms useful for learning within our methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Adamczyk, P. D., & Bailey, B. P. (2004). If not now, when? The effects of interruption at different moments within task execution. In Proc. of CHI’04, Vienna, Austria, April 24–29 (pp. 271–278).

  2. Adomavicius G., Tuzhulin A. (2005) Toward the next generation of recommender systems: A survey of state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6): 734–749

    Article  Google Scholar 

  3. Akyildiz I. F., Pompili D., Melodia T. (2005) Underwater acoustic sensor networks: Research challenges. Ad hoc Networks 3(3): 257–279

    Article  Google Scholar 

  4. Araya-Lopez, M., Buffet, O., Thomas, V., & Charpillet, F. (2010). A POMDP extension with belief-dependent rewards. In Proc. of NIPS’10.

  5. Arisha K., Youssef M., Younis M. (2002) Energy-aware TDMA-based MAC for sensor networks. In: Karri R., Goodman D. (eds) System-level power optimization for wireless multimedia communication. Kluwer Academic Publishers, Norwell, MA, pp 21–40

    Chapter  Google Scholar 

  6. Bernstein D. S., Givan R., Immerman N., Zilberstein S. (2002) The complexity of decentralized control of Markov decision processes. Mathematics of Operations Research 27(4): 819–840

    Article  MathSciNet  MATH  Google Scholar 

  7. Boutilier, C. (2002). A POMDP formulation of preference elicitation problems. In Proc. of AAAI’02 (pp. 239–246).

  8. Brafman R. I., Tennenholtz M. (2002) R-max—a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research 3: 213–231

    MathSciNet  Google Scholar 

  9. Casper J., Murphy R. R. (2003) Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center. IEEE Transactions on SMC Part B: Cybernetics 33(3): 367–385

    Article  Google Scholar 

  10. Chalupsky, H., et al. (2001). Electric Elves: Applying agent technology to support human organizations. In Proc. of IAAI’01, Seattle, WA, August 7–9 (pp. 51–58).

  11. Cox M. T., Raja A. (2011) Metareasoning: An introduction. In: Cox M., Raja A. (eds) Metareasoning: Thinking about thinking. MIT Press, Cambridge, MA, pp 3–14

    Google Scholar 

  12. Doshi, F., & Roy, N. (2008). The permutable POMDP: Fast solutions to POMDPs for preference elicitation. Proc. of AAMAS’08 (pp. 493–500).

  13. Ermon, S., et al. (2010). Playing games against nature: optimal policies for renewable resource allocation. In Proc. of UAI’10.

  14. Fowler H. J., Leland W. E. (1991) Local area network traffic characteristics, with implications for broadband network congestion management. IEEE Journal on Selected Areas of Communications 9(7): 1139–1149

    Article  Google Scholar 

  15. Gers F. A., Schmidhuber J., Cummins J. (2000) Learning to forget: Continual prediction with LSTM. Neural Computation 12(10): 2451–2471

    Article  Google Scholar 

  16. Grass, J., & Zilberstein, S. (1997). Value-driven information gathering. In Proc. of AAAI workshop on building resource-bounded reasoning systems.

  17. Grass J., Zilberstein S. (2000) A value-driven system for autonomous information gathering. Journal of Intelligent Information Systems 14: 5–27

    Article  Google Scholar 

  18. Guo, A. (2003). Decision-theoretic active sensing for autonomous agents. In Proc. of AAMAS’03 (pp. 1002–1003).

  19. Hochreiter S., Schmidhuber J. (1997) Long short-term memory. Neural Computation 9: 1735–1780

    Article  Google Scholar 

  20. Hoey, J., et al. (2007). Assisting persons with dementia during handwashing using a partially observable Markov decision process. In Proc. of ICVS’07.

  21. Josang A. (2001) A logic for uncertain probabilities. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9: 279–311

    MathSciNet  Google Scholar 

  22. Kaelbling L. P., Littman M. L., Moore W. (1996) Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4: 237–285

    Google Scholar 

  23. Kaelbling L. P., Littman M. L., Cassandra A. R. (1998) Planning and acting in partially observable stochastic domains. Artificial Intelligence 101: 99–134

    Article  MathSciNet  MATH  Google Scholar 

  24. Khandaker N., Soh L.-K., Miller L. D., Eck A., Jiang H. (2011) Lessons learned from comprehensive deployments of multiagent CSCL applications I-MINDS and ClassroomWiki. IEEE Transactions on Learning Technologies 4(1): 47–58

    Article  Google Scholar 

  25. Klein J., Moon Y., Picard R. W. (2002) This computer responds to user frustration: Theory, design, and results. Interacting with Computers 14: 119–140

    Article  Google Scholar 

  26. Krause, A., & Guestrin, C. (2005). Optimal nonmyopic value of information in graphical models—efficient algorithms and theoretical limits. In Proc. of IJCAI’05 (pp. 1339–1345).

  27. Krause, A., & Guestrin, C. (2007). Near-optimal observation selection using submodular functions. In Proc. of AAAI’07.

  28. Krause A., Guestrin C. (2009) Optimizing sensing: From water to the web. IEEE Computer 42(8): 38–45

    Article  Google Scholar 

  29. Krause A. et al (2008) Robust submodular observation selection. Journal of Machine Learning Research 9: 2761–2801

    MATH  Google Scholar 

  30. Landeldt, B., Sookavantana, P., & Seneviratne, A. (2000). The case for a hybrid passive/active network monitoring scheme in the wireless Internet. In Proc. of ICON’00 (pp. 139–143).

  31. Lesser V. et al (2000) BIG: An agent for resource-bounded information gathering and decision making. Artificial Intelligence 118: 197–244

    Article  MATH  Google Scholar 

  32. Mark, G., Gudith, D., & Klocke, U. (2008). The cost of interrupted work: More speed and stress. In Proc. of CHI’08 (pp. 107–110).

  33. Monostori L., Vancza J., Kumara S. R. T. (2006) Agent-based systems for manufacturing. CIRP Annals: Manufacturing Technology 55(2): 697–720

    Article  Google Scholar 

  34. Myers K. L. et al (2007) An intelligent personal assistant for task and time management. AI Magazine 28(2): 47–61

    Google Scholar 

  35. North M. J., Collier N. T., Vos J. R. (2006) Experiences creating three implementations of the Repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation 16: 1–25

    Article  Google Scholar 

  36. Padhy, P., Dash, R. K., Martinez, K., & Jennings, N. R. (2006). A utility-based sensing and communication model for a glacial sensor network. In Proc AAMAS’06, Hakodate, Japan, May 8–12 (pp. 1353–1360).

  37. Pineau, J., Gordon, G., & Thrun, S. (2003). Point-based value iteration: An anytime algorithm for POMDPs. In Proc. of IJCAI’03 (pp. 1025–1032).

  38. Pollack, M. E., & Ringuette, M. (1990). Introducing the tileworld: Experimentally evaluating agent architectures. In Proc. of AAAI’90 (pp. 183–189).

  39. Raja A., Lesser V. (2007) A framework for meta-level control in multi-agent systems. JAAMAS 15: 147–196

    Google Scholar 

  40. Ross, S., Chaib-draa, B., & Pineau, J. (2007). Bayes-adaptive POMDPs. In Proc. of NIPS’07.

  41. Ross S., Pineau J., Paquet S., Chaib-draa B. (2008) Online planning algorithms for POMDPs. Journal of Artificial Intelligence Research 32: 663–704

    MathSciNet  MATH  Google Scholar 

  42. Rumelhart D. E., Hinton G. E., Williams R. J. (1986) Learning internal representations by error propogation. In: Rumelhart D. E., McClelland J. L. (eds) Parallel distributed processing: explorations in the microstructure of cognitions. MIT Press, Cambridge, MA, pp 318–362

    Google Scholar 

  43. Shah, R. C., & Rabaey, J. M. (2002). Energy aware routing for low energy ad hoc sensor networks. In Proc. of WCNC’02, March 17–21 (pp. 350–355).

  44. Smith, T., & Simmons, R. (2004). Heuristic search value iteration for POMDPs. In Proc. UAI’04 (pp. 520–527).

  45. Spaan, M. T. J. (2008). Cooperative active perception using POMDPs. In AAAI 2008 workshop on advancements in POMDP solvers.

  46. Sutton R. S., Barto A. G. (1998) Reinforcement learning: An introduction. MIT Press, Cambridge, MA

    Google Scholar 

  47. The Biofinity Project. (2010). Retrieved March 7, 2011, from http://biofinity.unl.edu.

  48. Watkins, C. J. (1989). Learning from delayed rewards. PhD Thesis, Cambridge University.

  49. Werbos P. J. (1990) Backpropogation through time: What it does and how to do it. Proceedings of the IEEE 78(10): 1550–1560

    Article  Google Scholar 

  50. Weyns D., Steegmans E., Holvoet T. (2004) Towards active perception in situated multi-agent systems. Applied Artificial Intelligence 18: 867–883

    Article  Google Scholar 

  51. Weyns, D., Helleboogh, A., & Holvoet, T. (2005). The packet-world: A test bed for investigating situated multi-agent systems. In R. Unland, M. Klusch, & M. Calisti (Eds.), Software agent-based applications, platforms, and development kits (pp. 383–408).

  52. Wierstra, D., Foerster, A., Peters, J., & Schmidhuber, J. (2007). Solving deep memory POMDPs with recurrent policy gradients. In Proc. of ICANN’07 (pp. 697-706).

  53. Williams R. J. (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning 8: 229–256

    MATH  Google Scholar 

  54. Williams J. D., Young S. (2007) Partially observable Markov decision processes for spoken dialog systems. Computer Speech and Language 21: 393–422

    Article  Google Scholar 

  55. Yorke-Smith, N., Saddati, S., Meyers, K. L., & Morley, D. N. (2009). Like an intuitive and courteous butler: A proactive personal agent for task management. In Proc. of AAMAS’09, Budapest, Hungary, May 13–15 (pp. 337–344).

  56. Zilberstein S. (1996) Resource-bounded sensing and planning in autonomous systems. Autonomous Robots 3: 31–48

    Article  Google Scholar 

  57. Zilberstein S. (2011) Metareasoning and bounded rationality. In: Cox M., Raja A. (eds) Metareasoning: Thinking about thinking. MIT Press, Cambridge, MA, pp 27–40

    Google Scholar 

  58. Zilberstein, S., & Russell, S. J. (1993). Anytime sensing, planning, and action: A practical model for robot control. Proc. of IJCAI’93 (pp. 1402–1407).

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Nebraska, 256 Avery Hall, Lincoln, NE, 68588-0115, USA

    Adam Eck & Leen-Kiat Soh

Authors
  1. Adam Eck
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Leen-Kiat Soh
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Adam Eck.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eck, A., Soh, LK. Observer effect from stateful resources in agent sensing. Auton Agent Multi-Agent Syst 26, 202–244 (2013). https://doi.org/10.1007/s10458-011-9189-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10458-011-9189-y

Keywords