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Frames, Multi-Agents and Good Behaviours in Planning Rationales

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Making Strategies in Spatial Planning

Part of the book series: Urban and Landscape Perspectives ((URBANLAND,volume 9))

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Abstract

Spatial planning still lacks of robust scientific attention to knowledge and knowledge-in-action coordination in multi-agent environments. This limitation is particularly invalidating, as the current generation of spatial plans aims at democratising its traditional expert and top-down approach and enhancing its knowledge contents and multi-logic potentials. At the forefront of knowledge engineering, distributed and multi-agent intelligence, unfortunately, when paying attention to coordination of multi-agent microtasks in task accomplishment is still short in the elaboration of the integrated social thoughts that are prerequisites of the new generation of knowledge-based interactive spatial plans.

In the first part this chapter analyses features and outcomes of Multiple Source Knowledge Acquisition and Integration (MSKI), considering that in knowledge-based spatial planning engineering there is increasing awareness of the typical rational and computational complexity.

The new strategic, interactive and strongly future-oriented and visionary socio-environmental planning, in which through cognitive sessions and forums a multiplicity of agents (stakeholders) interact to set and solve complex problems, is an interesting challenge to multi-agent coordination in knowledge engineering. The second part considers the frame problem and the generation of Multi-Agent Knowledge, taking into account of new knowledge and practical relevance of the cognitive experiments in problem-setting and/or solving. The third part explores the potentials of cooperation-competition dilemmas in strategic interactive planning.

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References

  • Allen, J., Kautz, H., Pelavin, R., Tenenber, J. (Eds.). (1991). Reasoning about plans. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Arrow, K. J. (1963). Social choice and individual values. New York: Wiley.

    Google Scholar 

  • Avlijas, N., Borri, D., & Monno, V. (2005) Facing the crisis in contexts in transition: Rethinking local development through experimentations of strategic visioning. Paper presented at the Conference of the Regional Studies Association Regional Growth Agendas, University of Aalborg, Aalborg.

    Google Scholar 

  • Bacchus, F., & Kabanza, F. (2000). Using temporal logics to express search control knowledge for planning. Artificial Intelligence, 116(1), 123–191.

    Article  Google Scholar 

  • Baral, C., Kreinovich, V., & Trejo, R. (2000). Computational complexity of planning and approximate planning in the presence of incompleteness. Artificial Intelligence, 122(1), 241–242.

    Article  Google Scholar 

  • Barbanente, A., & Borri, D. (2000). Reviewing self-sustainability. Plurimondi, II(4), 5–19.

    Google Scholar 

  • Barbanente, A., Borri, D., & Concilio, G. (2001). Escapable dilemmas in planning: Decisions vs. transactions. In H. Voogd (Ed.), Recent developments in evaluation (pp. 355–376). Groningen: Geopress.

    Google Scholar 

  • Bauer, M., Biundo, S., Dengler, D., Hecking, M., Koehler, J., & Merziger, G. (1991). Integrated plan generation and recognition. A logic-based approach. Informatik-Fachberichte, 291, 266–277.

    Article  Google Scholar 

  • Binetti, M., Borri, D., Circella, G., & Mascia, M. (2005) Does prospect theory improve the understanding of transit user behaviour? In: Proceedings of the 9th Conference on Computers in Urban Planning and Urban Management (CUPUM), London.

    Google Scholar 

  • Blum, A., & Furst, M. L. (1995) Fast planning through planning graph analysis. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI-95), Montreal, pp. 1636–1642.

    Google Scholar 

  • Bonet, B., Loerincs, G., & Geffner, H. (1997) A robust and fast action selection mechanism for planning. In: Proceedings of the 14th National Conference on Artificial Intelligence and 9th Innovative Applications of Artificial Intelligence Conference (AAAI-97/IAAI-97), pp. 714–719.

    Google Scholar 

  • Borri, D. (2001). Planning in evolution. In N. Maiellaro (Ed.), Towards sustainable building (pp. 3–10). Dordrecht: Kluwer.

    Google Scholar 

  • Borri, D. (2002). Intelligent learning devices in planning. In K. Alexiou & T. Zamenopoulos (Eds.), Proceedings of the seminar on computational models in design and planning support. London: Center for Advanced Spatial Analysis, University College London.

    Google Scholar 

  • Borri, D., Camarda, D., & De Liddo, A. (2004). Envisioning environmental futures: Multi-agent knowledge generation, frame problem and cognitive mapping. Lectures Notes in Computer Science, 3190, 230–237.

    Article  Google Scholar 

  • Borri, D., Camarda, D., & De Liddo, A. (2005c). Mobility in environmental planning: An integrated multi-agent approach. Lecture Notes in Computer Science, 3675, 119–129.

    Article  Google Scholar 

  • Borri, D., Camarda, D., & Grassini, L. (2005a). Complex knowledge in the environmental domain: Building intelligent architectures for water management. Lecture Notes in Computer Science, 353, 762–772.

    Article  Google Scholar 

  • Borri, D., Camarda, D., & Grassini, L. (2006). Distributed knowledge in environmental planning: A hybrid IT-based approach to building future scenarios. Group Decision and Negotiation, 15(6), 557–580.

    Article  Google Scholar 

  • Borri, D., & Cera, M. (2005). An intelligent hybrid agent for medical emergency vehicles. Navigation in urban spaces. In P. van Oosterom, S. Zlatanova, & E. M. Fendel (Eds.), Geoinformation for disaster management (pp. 951–964). Berlin: Springer.

    Chapter  Google Scholar 

  • Borri, D., Concilio, G., Selicato, F., & Torre, C. (2005b). Ethical and moral reasoning and dilemmas in evaluation processes: Perspectives for intelligent agents. In D. Miller & D. Patassini (Eds.), Beyond benefit cost analysis. Accounting for non-market values in planning evaluation (pp. 249–277). Brookfield, VT: Ashgate.

    Google Scholar 

  • Borri, D., Grassini, L., & Starkl, M. (2009) Technological innovations and decision making changes in the water sector: Experiences from India. Paper presented at the Palestinian Water Authority 2nd International Conference on Water Values and Rights, Ramallah.

    Google Scholar 

  • Chapman, D. (1987). Planning with conjunctive goals. Artificial Intelligence, 32(3), 333–377.

    Article  Google Scholar 

  • Cohen, P. R. (1995). Empirical methods for artificial intelligence. Cambridge: MIT Press.

    Google Scholar 

  • Damasio, A. R. (1995). Descartes’ error. New York: Avon.

    Google Scholar 

  • Durfee, E. H. (1988). Coordination of distributed problem solvers. Dordrecht: Kluwer.

    Book  Google Scholar 

  • Fagin, R., Halpern, J., Moses, Y., & Vardi, M. (1995). Reasoning about knowledge. Cambridge: MIT Press.

    Google Scholar 

  • Faludi, A. (1973). Planning theory. Oxford: Pergamon.

    Google Scholar 

  • Faludi, A. (1987). A decision-centred view of environmental planning. Oxford: Pergamon.

    Google Scholar 

  • Ferber, J. (1997). Multi-agent decision support systems. London: Addison-Wesley.

    Google Scholar 

  • Fikes, R. E., & Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3–4), 189–208.

    Article  Google Scholar 

  • Finger, J. (1986). Exploiting constraints in design synthesis. Ph.D. Thesis. Stanford, CA: Department of Computer Science, Stanford University.

    Google Scholar 

  • Forester, J. (1989). Planning in the face of power. Berkeley, CA: University of California Press.

    Google Scholar 

  • Forester, J. (1999). The deliberative practitioner. Cambridge: MIT Press.

    Google Scholar 

  • Friedmann, J. (1981). The good society. Cambridge: The MIT Press.

    Google Scholar 

  • Friedmann, J. (1987). Planning in the public domain. From knowledge to action. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Gelfond, M., & Lifschitz, V. (1993). Representing actions and change by logic programs. Journal of Logic Programming, 17(2–4), 301–323.

    Article  Google Scholar 

  • Ginsberg, M. L. (1989a). Universal planning: An (almost) universally bad idea. AI Magazine, 10(4), 40–44.

    Google Scholar 

  • Ginsberg, M. L. (1989b). Ginsberg replies to chapman and schoppers. AI Magazine, 10(4), 61–62.

    Google Scholar 

  • Giunchiglia, F., & Spalazzi, L. (1999). Intelligent planning: A decomposition and abstraction based approach to classical planning. Artificial Intelligence, 111(1–2), 329–338.

    Article  Google Scholar 

  • Green, C. (1969) Applications of theorem proving to problem solving. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI 69), Washington, p. 219.

    Google Scholar 

  • Hammond, K. J. (1990). Case-based planning: A framework for planning from experience. Cognitive Science, 14(3), 385–443.

    Article  Google Scholar 

  • Healey, P. (1997). Collaborative planning. Shaping places in fragmented societies. London: MacMillan.

    Google Scholar 

  • Horty, J. F., & Pollack, M. E. (2001). Evaluating new options in the context of existing plans. Artificial Intelligence, 127(2), 199–220.

    Article  Google Scholar 

  • Ishida, T. (1997). Real time search for learning autonomous agents. Dordrecht: Kluwer.

    Google Scholar 

  • Jennings, N. R., Wooldridge, M. (Eds.). (1998). Agent technology: Foundations, applications, and markets. Berlin: Springer.

    Google Scholar 

  • Jonsson, P., Haslum, P., & Backstrom, C. (2000). Towards efficient universal planning: A randomized approach. Artificial Intelligence, 117(1), 1–29.

    Article  Google Scholar 

  • Koehler, J., Nebel, B., Hoffmann, J., & Dimopoulos, Y. (1997) Extending planning graphs to an ADL sub-set. In: Proceedings of the 4th European Conference on Planning (ECP-97), Toulouse, pp. 275–287.

    Google Scholar 

  • Kolodner, J. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Laird, J., Newell, A., & Rosenbloom, P. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, 33(1), 1–67.

    Article  Google Scholar 

  • Latouche, S. (1991). La Planéte des Naufragés: Essai sur l’Après-Développement. Paris: La Découverte.

    Google Scholar 

  • Lewin, K. (1948). Resolving social conflicts: Selected papers on group dynamics. New York: Harpres and Bros.

    Google Scholar 

  • McCarthy, J. (1977) Epistemological problems of artificial intelligence. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI-77), Cambridge, pp. 1038–1044.

    Google Scholar 

  • McCarthy, J., & Hayes, P. (1969). Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer & D. Michie (Eds.), Machine intelligence 4 (pp. 463–502). Edinburgh: Edinburgh University Press.

    Google Scholar 

  • McIlraith, S. (2000). Integrating actions and state constraints: A closed-form solution to the ramification problem (sometimes). Artificial Intelligence, 116(1), 87–121.

    Article  Google Scholar 

  • Minsky, M. L. (1986). Society of mind. New York: Simon and Schuster.

    Google Scholar 

  • Minton, S. (1988). Learning search control knowledge. Dordrecht: Kluwer.

    Book  Google Scholar 

  • Papadimitriou, C. H. (1994). Computational complexity. Reading, MA: Addison Wesley.

    Google Scholar 

  • Pearl, J. (1985). Heuristics: Intelligent search strategies for computer problem solving. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Pednault, E. (1988). Synthesizing plans that contain actions with context-dependent effects. Computational Intelligence, 4(4), 356–372.

    Article  Google Scholar 

  • Pednault, E. (1989) ADL: Exploring the middle ground between STRIPS and the situation calculus. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, Toronto, pp. 324–332.

    Google Scholar 

  • Reiter, R. (2001). Knowledge in action: Logical foundations for specifying and implementing dynamical systems. Cambridge: MIT Press.

    Google Scholar 

  • Rosenschein, S. (1981) Plan synthesis: A logical approach. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI-81), Vancouver, pp. 359–380.

    Google Scholar 

  • Russell, S., & Norvig, P. (1995). Artificial intelligence. A modern approach. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Sacerdoti, E. D. (1977). A structure for plans and behaviour. New York: Elsevier/North-Holland.

    Google Scholar 

  • Sandercock, L. (1998). Towards cosmopolis. New York: Wiley.

    Google Scholar 

  • Schank, R. C. (1982). Dynamic memory: A theory of learning in computers and people. Cambridge: Cambridge University Press.

    Google Scholar 

  • Schoppers, M. J. (1987) Universal plans for reactive robots in unpredictable environments. In Proceedings of IJCAI-87, Milano, pp. 1039–1046.

    Google Scholar 

  • Schubert, L. K. (1990). Monotonic solution of the frame problem in the situation calculus: An efficient method for worlds with fully specified actions. In H. E. Kyburg, R. P. Loui, & G. N. Carlson (Eds.), Knowledge representation and defeasible reasoning (pp. 23–67). Dordrecht: Kluwer Academic Press.

    Chapter  Google Scholar 

  • Schön, D. A. (1991). The reflective turn: Case studies in and on educational practice. New York: Teacher’s College Press.

    Google Scholar 

  • Selman, B. (1994) Near-optimal plans, tractability, and reactivity. In: Proceedings of the 4th International Conference on the Principles of Knowledge Representation and Reasoning, Bonn, pp. 521–529.

    Google Scholar 

  • Shakun, M. (1999). Consciousness, spirituality, and right decision/negotiation in purposeful complex adaptive systems. Group Decision and Negotiation, 8(1), 1–15.

    Article  Google Scholar 

  • Shanahan, M. (1997). Solving the frame problem. Cambridge: MIT Press.

    Google Scholar 

  • Simon, H. A. (1982). Models of bounded rationality. Cambridge: MIT Press.

    Google Scholar 

  • Soucek, B. (1997). Quantum mind networks. Split: FESB.

    Google Scholar 

  • Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning. Cambridge: MIT Press.

    Google Scholar 

  • Veloso, M., Carbonell, J., Pérez, A., Borrajo, D., Fink, E., & Blythe, J. (1995). Integrating planning and learning: The PRODIGY architecture. Journal of Experimental and Theoretical Artificial Intelligence, 7(1), 81–120.

    Article  Google Scholar 

  • Watts, D. J. (1999). Small worlds, the dynamics of networks between order and randomness. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Wilkins, D. E. (1988). Practical planning: Extending the classical AI planning paradigm. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Yang, Q. (1997). Intelligent planning: A decomposition and abstraction based approach. Berlin: Springer.

    Google Scholar 

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Acknowledgements

Sections of this chapter refer to papers presented in various occasions. In particular Sections 14.1, 14.2 and 14.3 are a re-elaboration of an invited lecture that was presented in a seminar on multi-agents in planning organised by professor Lidia Diappi at the Polytechnic of Milan in 2002 and Section 14.4 is a re-elaboration of an invited lecture that was presented in a seminar on planning evolution organised by professor Corrado Zoppi at the University of Cagliari in 2007.

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Borri, D. (2010). Frames, Multi-Agents and Good Behaviours in Planning Rationales. In: Cerreta, M., Concilio, G., Monno, V. (eds) Making Strategies in Spatial Planning. Urban and Landscape Perspectives, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3106-8_14

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