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.
Arrow, K. J. (1963). Social choice and individual values. New York: Wiley.
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.
Bacchus, F., & Kabanza, F. (2000). Using temporal logics to express search control knowledge for planning. Artificial Intelligence, 116(1), 123–191.
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.
Barbanente, A., & Borri, D. (2000). Reviewing self-sustainability. Plurimondi, II(4), 5–19.
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.
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.
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.
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.
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.
Borri, D. (2001). Planning in evolution. In N. Maiellaro (Ed.), Towards sustainable building (pp. 3–10). Dordrecht: Kluwer.
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.
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.
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.
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.
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.
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.
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.
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.
Chapman, D. (1987). Planning with conjunctive goals. Artificial Intelligence, 32(3), 333–377.
Cohen, P. R. (1995). Empirical methods for artificial intelligence. Cambridge: MIT Press.
Damasio, A. R. (1995). Descartes’ error. New York: Avon.
Durfee, E. H. (1988). Coordination of distributed problem solvers. Dordrecht: Kluwer.
Fagin, R., Halpern, J., Moses, Y., & Vardi, M. (1995). Reasoning about knowledge. Cambridge: MIT Press.
Faludi, A. (1973). Planning theory. Oxford: Pergamon.
Faludi, A. (1987). A decision-centred view of environmental planning. Oxford: Pergamon.
Ferber, J. (1997). Multi-agent decision support systems. London: Addison-Wesley.
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.
Finger, J. (1986). Exploiting constraints in design synthesis. Ph.D. Thesis. Stanford, CA: Department of Computer Science, Stanford University.
Forester, J. (1989). Planning in the face of power. Berkeley, CA: University of California Press.
Forester, J. (1999). The deliberative practitioner. Cambridge: MIT Press.
Friedmann, J. (1981). The good society. Cambridge: The MIT Press.
Friedmann, J. (1987). Planning in the public domain. From knowledge to action. Princeton, NJ: Princeton University Press.
Gelfond, M., & Lifschitz, V. (1993). Representing actions and change by logic programs. Journal of Logic Programming, 17(2–4), 301–323.
Ginsberg, M. L. (1989a). Universal planning: An (almost) universally bad idea. AI Magazine, 10(4), 40–44.
Ginsberg, M. L. (1989b). Ginsberg replies to chapman and schoppers. AI Magazine, 10(4), 61–62.
Giunchiglia, F., & Spalazzi, L. (1999). Intelligent planning: A decomposition and abstraction based approach to classical planning. Artificial Intelligence, 111(1–2), 329–338.
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.
Hammond, K. J. (1990). Case-based planning: A framework for planning from experience. Cognitive Science, 14(3), 385–443.
Healey, P. (1997). Collaborative planning. Shaping places in fragmented societies. London: MacMillan.
Horty, J. F., & Pollack, M. E. (2001). Evaluating new options in the context of existing plans. Artificial Intelligence, 127(2), 199–220.
Ishida, T. (1997). Real time search for learning autonomous agents. Dordrecht: Kluwer.
Jennings, N. R., Wooldridge, M. (Eds.). (1998). Agent technology: Foundations, applications, and markets. Berlin: Springer.
Jonsson, P., Haslum, P., & Backstrom, C. (2000). Towards efficient universal planning: A randomized approach. Artificial Intelligence, 117(1), 1–29.
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.
Kolodner, J. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmann.
Laird, J., Newell, A., & Rosenbloom, P. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, 33(1), 1–67.
Latouche, S. (1991). La Planéte des Naufragés: Essai sur l’Après-Développement. Paris: La Découverte.
Lewin, K. (1948). Resolving social conflicts: Selected papers on group dynamics. New York: Harpres and Bros.
McCarthy, J. (1977) Epistemological problems of artificial intelligence. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI-77), Cambridge, pp. 1038–1044.
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.
McIlraith, S. (2000). Integrating actions and state constraints: A closed-form solution to the ramification problem (sometimes). Artificial Intelligence, 116(1), 87–121.
Minsky, M. L. (1986). Society of mind. New York: Simon and Schuster.
Minton, S. (1988). Learning search control knowledge. Dordrecht: Kluwer.
Papadimitriou, C. H. (1994). Computational complexity. Reading, MA: Addison Wesley.
Pearl, J. (1985). Heuristics: Intelligent search strategies for computer problem solving. Reading, MA: Addison-Wesley.
Pednault, E. (1988). Synthesizing plans that contain actions with context-dependent effects. Computational Intelligence, 4(4), 356–372.
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.
Reiter, R. (2001). Knowledge in action: Logical foundations for specifying and implementing dynamical systems. Cambridge: MIT Press.
Rosenschein, S. (1981) Plan synthesis: A logical approach. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI-81), Vancouver, pp. 359–380.
Russell, S., & Norvig, P. (1995). Artificial intelligence. A modern approach. Englewood Cliffs, NJ: Prentice Hall.
Sacerdoti, E. D. (1977). A structure for plans and behaviour. New York: Elsevier/North-Holland.
Sandercock, L. (1998). Towards cosmopolis. New York: Wiley.
Schank, R. C. (1982). Dynamic memory: A theory of learning in computers and people. Cambridge: Cambridge University Press.
Schoppers, M. J. (1987) Universal plans for reactive robots in unpredictable environments. In Proceedings of IJCAI-87, Milano, pp. 1039–1046.
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.
Schön, D. A. (1991). The reflective turn: Case studies in and on educational practice. New York: Teacher’s College Press.
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.
Shakun, M. (1999). Consciousness, spirituality, and right decision/negotiation in purposeful complex adaptive systems. Group Decision and Negotiation, 8(1), 1–15.
Shanahan, M. (1997). Solving the frame problem. Cambridge: MIT Press.
Simon, H. A. (1982). Models of bounded rationality. Cambridge: MIT Press.
Soucek, B. (1997). Quantum mind networks. Split: FESB.
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning. Cambridge: MIT Press.
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.
Watts, D. J. (1999). Small worlds, the dynamics of networks between order and randomness. Princeton, NJ: Princeton University Press.
Wilkins, D. E. (1988). Practical planning: Extending the classical AI planning paradigm. San Mateo, CA: Morgan Kaufmann.
Yang, Q. (1997). Intelligent planning: A decomposition and abstraction based approach. Berlin: Springer.
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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