Nanoclustering as a dominant feature of plasma membrane organization

Early studies have revealed that some mammalian plasma membrane proteins exist in small nanoclusters. The advent of super-resolution microscopy has corroborated and extended this picture, and led to the suggestion that many, if not most, membrane proteins are clustered at the plasma membrane at nanoscale lengths. In this Commentary, we present selected examples of glycosylphosphatidyl-anchored proteins, Ras family members and several immune receptors that provide evidence for nanoclustering. We advocate the view that nanoclustering is an important part of the hierarchical organization of proteins in the plasma membrane. According to this emerging picture, nanoclusters can be organized on the mesoscale to form microdomains that are capable of supporting cell adhesion, pathogen binding and immune cell-cell recognition amongst other functions. Yet, a number of outstanding issues concerning nanoclusters remain open, including the details of their molecular composition, biogenesis, size, stability, function and regulation. Notions about these details are put forth and suggestions are made about nanocluster function and why this general feature of protein nanoclustering appears to be so prevalent.Postprint (published version

A Better-response Strategy for Self-interested Planning Agents

[EN] When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents¿ strategic behavior considering the interactions as part of the agents¿ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents¿ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.This work is supported by the GLASS project TIN2014-55637-C2-2-R of the Spanish MINECO and the Prometeo project II/2013/019 funded by the Valencian Government.Jordán, J.; Torreño Lerma, A.; De Weerdt, M.; Onaindia De La Rivaherrera, E. (2018). A Better-response Strategy for Self-interested Planning Agents. Applied Intelligence. 48(4):1020-1040. https://doi.org/10.1007/s10489-017-1046-5S10201040484Aghighi M, Bäckström C (2016) A multi-parameter complexity analysis of cost-optimal and net-benefit planning. In: Proceedings of the Twenty-Sixth International Conference on International Conference on Automated Planning and Scheduling. AAAI Press, London, pp 2–10Bercher P, Mattmüller R (2008) A planning graph heuristic for forward-chaining adversarial planning. In: ECAI, vol 8, pp 921–922Brafman RI, Domshlak C, Engel Y, Tennenholtz M (2009) Planning games. In: IJCAI 2009, Proceedings of the 21st international joint conference on artificial intelligence, pp 73–78Bylander T (1994) The computational complexity of propositional strips planning. Artif Intell 69(1):165–204Chen X, Deng X (2006) Settling the complexity of two-player nash equilibrium. In: 47th annual IEEE symposium on foundations of computer science, 2006. FOCS’06. IEEE, pp 261–272Chien S, Sinclair A (2011) Convergence to approximate nash equilibria in congestion games. Games and Economic Behavior 71(2):315–327de Cote EM, Chapman A, Sykulski AM, Jennings N (2010) Automated planning in repeated adversarial games. In: 26th conference on uncertainty in artificial intelligence (UAI 2010), pp 376–383Dunne PE, Kraus S, Manisterski E, Wooldridge M (2010) Solving coalitional resource games. Artif Intell 174(1):20–50Fabrikant A, Papadimitriou C, Talwar K (2004) The complexity of pure nash equilibria. In: Proceedings of the thirty-sixth annual ACM symposium on theory of computing, STOC ’04, pp 604–612Friedman JW, Mezzetti C (2001) Learning in games by random sampling. J Econ Theory 98(1):55–84Ghallab M, Nau D, Traverso P (2004) Automated planning: theory & practice. ElsevierGoemans M, Mirrokni V, Vetta A (2005) Sink equilibria and convergence. In: Proceedings of the 46th annual IEEE symposium on foundations of computer science, FOCS ’05, pp 142–154Hadad M, Kraus S, Hartman IBA, Rosenfeld A (2013) Group planning with time constraints. Ann Math Artif Intell 69(3):243–291Hart S, Mansour Y (2010) How long to equilibrium? the communication complexity of uncoupled equilibrium procedures. Games and Economic Behavior 69(1):107–126Helmert M (2003) Complexity results for standard benchmark domains in planning. Artif Intell 143(2):219–262Helmert M (2006) The fast downward planning system. J Artif Intell Res 26(1):191–246Jennings N, Faratin P, Lomuscio A, Parsons S, Wooldrige M, Sierra C (2001) Automated negotiation: prospects, methods and challenges. Group Decis Negot 10(2):199–215Johnson DS, Papadimtriou CH, Yannakakis M (1988) How easy is local search? J Comput Syst Sci 37 (1):79–100Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling, ICAPSJordán J, Onaindía E (2015) Game-theoretic approach for non-cooperative planning. In: 29th AAAI conference on artificial intelligence (AAAI-15), pp 1357–1363McDermott D, Ghallab M, Howe A, Knoblock C, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL: the planning domain definition language. Yale Center for Computational Vision and Control, New HavenMilchtaich I (1996) Congestion games with player-specific payoff functions. Games and Economic Behavior 13(1):111–124Monderer D, Shapley LS (1996) Potential games. Games and Economic Behavior 14(1):124–143Nigro N, Welch D, Peace J (2015) Strategic planning to implement publicly available ev charching stations: a guide for business and policy makers. Tech rep, Center for Climate and Energy SolutionsNisan N, Ronen A (2007) Computationally feasible vcg mechanisms. J Artif Intell Res 29(1):19–47Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory. Cambridge University Press, New YorkPapadimitriou CH (1994) On the complexity of the parity argument and other inefficient proofs of existence. J Comput Syst Sci 48(3):498–532Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177Rosenthal RW (1973) A class of games possessing pure-strategy nash equilibria. Int J Game Theory 2(1):65–67Shoham Y, Leyton-Brown K (2009) Multiagent systems: algorithmic, game-theoretic, and logical foundations. Cambridge University PressTorreño A, Onaindia E, Sapena Ó (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Torreño A, Onaindia E, Sapena Ó (2014) FMAP: distributed cooperative multi-agent planning. Appl Intell 41(2):606– 626Torreño A, Sapena Ó, Onaindia E (2015) Global heuristics for distributed cooperative multi-agent planning. In: ICAPS 2015. 25th international conference on automated planning and scheduling. AAAI Press, pp 225–233Von Neumann J, Morgenstern O (2007) Theory of games and economic behavior. Princeton University Pressde Weerdt M, Bos A, Tonino H, Witteveen C (2003) A resource logic for multi-agent plan merging. Ann Math Artif Intell 37(1):93–130Wooldridge M, Endriss U, Kraus S, Lang J (2013) Incentive engineering for boolean games. Artif Intell 195:418–43

FMAP: Distributed Cooperative Multi-Agent Planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, the Valencian Prometeo project II/2013/019, and the FPI-UPV scholarship granted to the first author by the Universitat Politecnica de Valencia.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). FMAP: Distributed Cooperative Multi-Agent Planning. Applied Intelligence. 41(2):606-626. https://doi.org/10.1007/s10489-014-0540-2S606626412Benton J, Coles A, Coles A (2012) Temporal planning with preferences and time-dependent continuous costs. In: Proceedings of the 22nd international conference on automated planning and scheduling (ICAPS). AAAI, pp 2–10Borrajo D. (2013) Multi-agent planning by plan reuse. In: Proceedings of the 12th international conference on autonomous agents and multi-agent systems (AAMAS). IFAAMAS, pp 1141–1142Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). AAAI, pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiagent Syst 19(3):297–331Bresina J, Dearden R, Meuleau N, Ramakrishnan S, Smith D, Washington R (2002) Planning under continuous time and resource uncertainty: a challenge for AI. In: Proceedings of the 18th conference on uncertainty in artificial intelligence (UAI). Morgan Kaufmann, pp 77–84Cox J, Durfee E (2009) Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst 5(4):373–408Crosby M, Rovatsos M, Petrick R (2013) Automated agent decomposition for classical planning. In: Proceedings of the 23rd international conference on automated planning and scheduling (ICAPS). AAAI, pp 46–54Dimopoulos Y, Hashmi MA, Moraitis P (2012) μ-satplan: Multi-agent planning as satisfiability. Knowl-Based Syst 29:54–62Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Gerevini A, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artif Intell 173(5-6):619–668Ghallab M, Nau D, Traverso P (2004) Automated planning. Theory and practice. Morgan KaufmannGünay A, Yolum P (2013) Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments. Appl Intell 39(3):489–509Helmert M (2004) A planning heuristic based on causal graph analysis. In: Proceedings of the 14th international conference on automated planning and scheduling ICAPS. AAAI, pp 161–170Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jannach D, Zanker M (2013) Modeling and solving distributed configuration problems: a CSP-based approach. IEEE Trans Knowl Data Eng 25(3):603–618Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kala R, Warwick K (2014) Dynamic distributed lanes: motion planning for multiple autonomous vehicles. Appl Intell:1–22Koehler J, Ottiger D (2002) An AI-based approach to destination control in elevators. AI Mag 23(3):59–78Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportvan der Krogt R (2009) Quantifying privacy in multiagent planning. Multiagent Grid Syst 5(4):451–469Kvarnström J (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi-Agent Syst 9(1–2):87–143Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res 20:1–59Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 1323–1330O’Brien P, Nicol R (1998) FIPA - towards a standard for software agents. BT Tech J 16(3):51–59Öztürk P, Rossland K, Gundersen O (2010) A multiagent framework for coordinated parallel problem solving. Appl Intell 33(2):132–143Pal A, Tiwari R, Shukla A (2013) Communication constraints multi-agent territory exploration task. Appl Intell 38(3):357–383Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177de la Rosa T, García-Olaya A, Borrajo D (2013) A case-based approach to heuristic planning. Appl Intell 39(1):184–201Sapena O, Onaindia E (2008) Planning in highly dynamic environments: an anytime approach for planning under time constraints. Appl Intell 29(1):90–109Sapena O, Onaindia E, Garrido A, Arangú M (2008) A distributed CSP approach for collaborative planning systems. Eng Appl Artif Intell 21(5):698–709Serrano E, Such J, Botía J, García-Fornes A (2013) Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl Intell:1–16Smith D, Frank J, Jónsson A (2000) Bridging the gap between planning and scheduling. Knowl Eng Rev 15(1):47–83Such J, García-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a privacy-enhancing agent platform. Eng Appl Artif Intell:96–109Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Torreño A, Onaindia E, Sapena O (2012) An approach to multi-agent planning with incomplete information. In: Proceedings of the 20th European conference on artificial intelligence (ECAI), vol 242. IOS Press, pp 762–767Torreño A, Onaindia E, Sapena O (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Van Der Krogt R, De Weerdt M (2005) Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). AAAI, pp 161–170de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiagent Grid Syst 5(4):345– 355Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10(5):673–685Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th international joint conference conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 798–80

A flexible coupling approach to multi-agent planning under incomplete information

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-012-0569-7Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, and the Valencian Prometeo project 2008/051.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). A flexible coupling approach to multi-agent planning under incomplete information. Knowledge and Information Systems. 38:141-178. https://doi.org/10.1007/s10115-012-0569-7S14117838Argente E, Botti V, Carrascosa C, Giret A, Julian V, Rebollo M (2011) An abstract architecture for virtual organizations: the THOMAS approach. Knowl Inf Syst 29(2):379–403Barrett A, Weld DS (1994) Partial-order planning: evaluating possible efficiency gains. Artif Intell 67(1):71–112Belesiotis A, Rovatsos M, Rahwan I (2010) Agreeing on plans through iterated disputes. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems. pp 765–772Bellifemine F, Poggi A, Rimassa G (2001) JADE: a FIPA2000 compliant agent development environment. In: Proceedings of the 5th international conference on autonomous agents (AAMAS). ACM, pp 216–217Blum A, Furst ML (1997) Fast planning through planning graph analysis. Artif Intell 90(1–2):281–300Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiag Syst 19(3):297–331Coles A, Coles A, Fox M, Long D (2010) Forward-chaining partial-order planning. In: Proceedings of the 20th international conference on automated planning and scheduling (ICAPS). pp 42–49Coles A, Fox M, Long D, Smith A (2008) Teaching forward-chaining planning with JavaFF. In: Colloquium on AI education, 23rd AAAI conference on artificial intelligenceCox J, Durfee E, Bartold T (2005) A distributed framework for solving the multiagent plan coordination problem. In: Proceedings of the 4th international joint conference on autonomous agents and multiagent systems (AAMAS). ACM, pp 821–827de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiag Grid Syst 5(4):345–355Decker K, Lesser VR (1992) Generalizing the partial global planning algorithm. Int J Coop Inf Syst 2(2):319–346desJardins M, Durfee E, Ortiz C, Wolverton M (1999) A survey of research in distributed continual planning. AI Mag 20(4):13–22Doshi P (2007) On the role of interactive epistemology in multiagent planning. In: Artificial intelligence and, pattern recognition. pp 208–213Dréo J, Savéant P, Schoenauer M, Vidal V (2011) Divide-and-evolve: the marriage of descartes and darwin. In: Proceedings of the 7th international planning competition (IPC). Freiburg, GermanyDurfee EH (2001) Distributed problem solving and planning. In: Multi-agents systems and applications: selected tutorial papers from the 9th ECCAI advanced course (ACAI) and agentLink’s third European agent systems summer school (EASSS), vol LNAI 2086. Springer, pp 118–149Durfee EH, Lesser V (1991) Partial global planning: a coordination framework for distributed hypothesis formation. IEEE Trans Syst Man Cybern Special Issue Distrib Sens Netw 21(5):1167–1183Ephrati E, Rosenschein JS (1996) Deriving consensus in multiagent systems. Artif Intell 87(1–2):21–74Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Fogués R, Alberola J, Such J, Espinosa A, Garcia-Fornes A (2010) Towards dynamic agent interaction support in open multiagent systems. In: Proceedings of the 2010 conference on artificial intelligence research and development: proceedings of the 13th international conference of the Catalan association for artificial intelligence’. IOS Press, pp 89–98Gerevini A, Long D (2006) Preferences and soft constraints in PDDL3. In: ICAPS workshop on planning with preferences and soft constraints, vol 6. Citeseer, pp 46–53Ghallab M, Howe A, Knoblock C, McDermott D, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL-the Planning Domain Definition Language. In: AIPS-98 planning committeeGmytrasiewicz P, Doshi P (2005) A framework for sequential planning in multi-agent settings. J Artif Intell Res 24:49–79Haslum P, Jonsson P (1999) Some results on the complexity of planning with incomplete information. In: Proceedings of the 5th European conference on, planning (ECP). pp 308–318Helmert M (2006) The fast downward planning system. J Artif Intell Res 26(1):191–246Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kambhampati S (1997) Refinement planning as a unifying framework for plan synthesis. AI Mag 18(2):67–97Kaminka GA, Pynadath DV, Tambe M (2002) Monitoring teams by overhearing: a multi-agent plan-recognition approach. J Artif Intell Res 17:83–135Kone M, Shimazu A, Nakajima T (2000) The state of the art in agent communication languages. Knowl Inf Syst 2(3):259–284Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportKraus S (1997) Beliefs, time and incomplete information in multiple encounter negotiations among autonomous agents. Ann Math Artif Intell 20(1–4):111–159Kumar A, Zilberstein S, Toussaint M (2011) Scalable multiagent planning using probabilistic inference. In: Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI)’. Barcelona, Spain, pp 2140–2146Kvarnström J. (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi Agent Syst 9(1):87–143Lipovetzky N, Geffner H (2011) Searching for plans with carefully designed probes. In: Proceedings of the 21th international conference on automated planning and scheduling (ICAPS)Micacchi C, Cohen R (2008) A framework for simulating real-time multi-agent systems. Knowl Inf Syst 17(2):135–166Nguyen N, Katarzyniak R (2009) Actions and social interactions in multi-agent systems. Knowl Inf Syst 18(2):133–136Nguyen X, Kambhampati S (2001) Reviving partial order planning. In: Proceedings of the 17th international joint conference on artificial intelligence (IJCAI). Morgan Kaufmann, pp 459–464Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). pp 1323–1330Pajares S, Onaindia E (2012) Defeasible argumentation for multi-agent planning in ambient intelligence applications. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems (AAMAS) pp 509–516Paolucci M, Shehory O, Sycara K, Kalp D, Pannu A (2000) A planning component for RETSINA agents. Intelligent Agents VI. Agent Theories Architectures, and Languages pp 147–161Parsons S, Sierra C, Jennings N (1998) Agents that reason and negotiate by arguing. J Logic Comput 8(3):261Penberthy J, Weld D (1992) UCPOP: a sound, complete, partial order planner for ADL. In: Proceedings of the 3rd international conference on principles of knowledge representation and reasoning (KR). Morgan Kaufmann, pp 103–114Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177Sycara K, Pannu A (1998) The RETSINA multiagent system (video session): towards integrating planning, execution and information gathering. In: Proceedings of the 2nd international conference on autonomous agents (Agents). ACM, pp 350–351Tambe M (1997) Towards flexible teamwork. J Artif Intell Res 7:83–124Tang Y, Norman T, Parsons S (2010) A model for integrating dialogue and the execution of joint plans. Argumentation in multi-agent systems, pp 60–78Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Van Der Krogt R, De Weerdt M (2005), Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). pp 161–170Weld D (1994) An introduction to least commitment planning. AI Mag 15(4):27Weld D (1999) Recent advances in AI planning. AI Mag 20(2):93–123Wilkins D, Myers K (1998) A multiagent planning architecture. In: Proceedings of the 4th international conference on artificial intelligence planning systems (AIPS), pp 154–162Wu F, Zilberstein S, Chen X (2011) Online planning for multi-agent systems with bounded communication. Artif Intell 175(2):487–511Younes H, Simmons R (2003) VHPOP: versatile heuristic partial order planner. J Artif Intell Res 20: 405–430Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th conference on autonomous agents and multiagent systems (AAMAS

Nanoclustering as a dominant feature of plasma membrane organization

Early studies have revealed that some mammalian plasma membrane proteins exist in small nanoclusters. The advent of super-resolution microscopy has corroborated and extended this picture, and led to the suggestion that many, if not most, membrane proteins are clustered at the plasma membrane at nanoscale lengths. In this Commentary, we present selected examples of glycosylphosphatidyl-anchored proteins, Ras family members and several immune receptors that provide evidence for nanoclustering. We advocate the view that nanoclustering is an important part of the hierarchical organization of proteins in the plasma membrane. According to this emerging picture, nanoclusters can be organized on the mesoscale to form microdomains that are capable of supporting cell adhesion, pathogen binding and immune cell-cell recognition amongst other functions. Yet, a number of outstanding issues concerning nanoclusters remain open, including the details of their molecular composition, biogenesis, size, stability, function and regulation. Notions about these details are put forth and suggestions are made about nanocluster function and why this general feature of protein nanoclustering appears to be so prevalent

Nanoclustering as a dominant feature of plasma membrane organization

Early studies have revealed that some mammalian plasma membrane proteins exist in small nanoclusters. The advent of super-resolution microscopy has corroborated and extended this picture, and led to the suggestion that many, if not most, membrane proteins are clustered at the plasma membrane at nanoscale lengths. In this Commentary, we present selected examples of glycosylphosphatidyl-anchored proteins, Ras family members and several immune receptors that provide evidence for nanoclustering. We advocate the view that nanoclustering is an important part of the hierarchical organization of proteins in the plasma membrane. According to this emerging picture, nanoclusters can be organized on the mesoscale to form microdomains that are capable of supporting cell adhesion, pathogen binding and immune cell-cell recognition amongst other functions. Yet, a number of outstanding issues concerning nanoclusters remain open, including the details of their molecular composition, biogenesis, size, stability, function and regulation. Notions about these details are put forth and suggestions are made about nanocluster function and why this general feature of protein nanoclustering appears to be so prevalent

Cooperative Multi-Agent Planning: A survey

[EN] Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.This work is supported by the GLASS project Grant No. TIN2014-55637-C2-2-R MINECO of the Spanish Ministerio de Economia, Industria y Competitividad, the Prometeo project II/2013/019 funded by the Valencian Government, and the four-year FPI-UPV research scholarship granted to the first author by the Universitat Politecnica de Valencia. Additionally, this research was partially supported by the Czech Science Foundation under Grant No. 15-20433Y CSF.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Komenda, A.; Tolba, M. (2017). Cooperative Multi-Agent Planning: A survey. ACM Computing Surveys. 50(6):84:1-84:32. https://doi.org/10.1145/3128584S84:184:32506Eyal Amir and Barbara Engelhardt. 2003. Factored planning. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI’03), Vol. 3. 929--935.J. Benton, Amanda J. Coles, and Andrew I. Coles. 2012. Temporal planning with preferences and time-dependent continuous costs. In Proceedings of the 22nd International Conference on Automated Planning and Scheduling (ICAPS’12). 2--10.Andrea Bonisoli, Alfonso E. Gerevini, Alessandro Saetti, and Ivan Serina. 2014. A privacy-preserving model for the multi-agent propositional planning problem. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). 973--974.Daniel Borrajo. 2013. Multi-agent planning by plan reuse. In Proceedings of the 12th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS’13). 1141--1142.Daniel Borrajo and Susana Fernández. 2015. MAPR and CMAP. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 1--3.Craig Boutilier and Ronen I. Brafman. 2001. Partial-order planning with concurrent interacting actions. J. Artific. Intell. Res. 14 (2001), 105--136.Ronen I. Brafman. 2015. A privacy preserving algorithm for multi-agent planning and search. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15). 1530--1536.Ronen I. Brafman and Carmel Domshlak. 2006. Factored planning: How, when, and when not. In Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference. 809--814.Ronen I. Brafman and Carmel Domshlak. 2008. From one to many: Planning for loosely coupled multi-agent systems. In Proceedings of the 18th International Conference on Automated Planning and Scheduling (ICAPS’08). 28--35.Isabel Cenamor, Tomás de la Rosa, and Fernando Fernández. 2014. IBACOP and IBACOP2 planner. In Proceedings of the International Planning Competition (IPC’14).Bradley J. Clement and Edmund H. Durfee. 1999. Top-down search for coordinating the hierarchical plans of multiple agents. In Proceedings of the 3rd Annual Conference on Autonomous Agents (AGENTS’99). ACM, New York, NY, 252--259.Daniel D. Corkill. 1979. Hierarchical planning in a distributed environment. In Proceedings of the 6th International Joint Conference on Artificial Intelligence (IJCAI’79). 168--175.Jeffrey S. Cox and Edmund H. Durfee. 2004. Efficient mechanisms for multiagent plan merging. In Proceedings of the 3rd Conference on Autonomous Agents and Multiagent Systems (AAMAS’04). 1342--1343.Jeffrey S. Cox and Edmund H. Durfee. 2009. Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst. 5, 4 (2009), 373--408.Matthew Crosby, Anders Jonsson, and Michael Rovatsos. 2014. A single-agent approach to multiagent planning. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). 237--242.Matthew Crosby, Michael Rovatsos, and Ronald P. A. Petrick. 2013. Automated agent decomposition for classical planning. In Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS’13). 46--54.Mathijs de Weerdt, André Bos, Hans Tonino, and Cees Witteveen. 2003. A resource logic for multi-agent plan merging. Ann. Math. Artific. Intell. 37, 1-2 (2003), 93--130.Mathijs de Weerdt and Bradley J. Clement. 2009. Introduction to planning in multiagent systems. Multiagent Grid Syst. 5, 4 (2009), 345--355.Keith Decker, Salim Khan, Carl Schmidt, Gang Situ, Ravi Makkena, and Dennis Michaud. 2002. BioMAS: A multi-agent system for genomic annotation. Int. J. Coop. Info. Syst. 11, 3 (2002), 265--292.Keith Decker and Victor R. Lesser. 1992. Generalizing the partial global planning algorithm. Int. J. Coop. Info. Syst. 2, 2 (1992), 319--346.Marie desJardins and Michael Wolverton. 1999. Coordinating a distributed planning system. AI Mag. 20, 4 (1999), 45--53.Marie E. desJardins, Edmund H. Durfee, Charles L. Ortiz, and Michael J. Wolverton. 1999. A survey of research in distributed continual planning. AI Mag. 20, 4 (1999), 13--22.Yannis Dimopoulos, Muhammad A. Hashmi, and Pavlos Moraitis. 2012. -SATPLAN: Multi-agent planning as satisfiability. Knowl.-Based Syst. 29 (2012), 54--62.Jürgen Dix, Héctor Muñoz-Avila, Dana S. Nau, and Lingling Zhang. 2003. IMPACTing SHOP: Putting an AI planner into a multi-agent environment. Ann. Math. Artific. Intell. 37, 4 (2003), 381--407.Edmund H. Durfee. 1999. Distributed Problem Solving and Planning, Gerhard Weiss (ed.). MIT Press,118--149.Edmund H. Durfee and Victor Lesser. 1991. Partial global planning: A coordination framework for distributed hypothesis formation. IEEE Trans. Syst. Man Cybernet. Spec. Issue Distrib. Sensor Netw. 21, 5 (1991), 1167--1183.Eithan Ephrati and Jeffrey S. Rosenschein. 1994. Divide and conquer in multi-agent planning. In Proceedings of the 12th National Conference on Artificial Intelligence (AAAI’94). 375--380.Eithan Ephrati and Jeffrey S. Rosenschein. 1997. A heuristic technique for multi-agent planning. Ann. Math. Artific. Intell. 20, 1–4 (1997), 13--67.Eric Fabre, Loïg Jezequel, Patrik Haslum, and Sylvie Thiébaux. 2010. Cost-optimal factored planning: Promises and pitfalls. In Proceedings of the 20th International Conference on Automated Planning and Scheduling (ICAPS’10). 65--72.Boi Faltings, Thomas Léauté, and Adrian Petcu. 2008. Privacy guarantees through distributed constraint satisfaction. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’08), Vol. 2. IEEE, 350--358.Richard Fikes and Nils J. Nilsson. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artific. Intell. 2, 3 (1971), 189--208.Daniel Fišer, Michal Štolba, and Antonín Komenda. 2015. MAPlan. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 8--10.Foundation for Intelligent Physical Agents. 2002. FIPA Interaction Protocol Specification. Retrieved from http://www.fipa.org/repository/ips.php3.Maria Fox and Derek Long. 2003. PDDL2.1: An extension to PDDL for expressing temporal planning domains. J. Artific. Intell. Res. 20 (2003), 61--124.Malik Ghallab, Adele Howe, Craig Knoblock, Drew McDermott, Ashwin Ram, Manuela M. Veloso, Daniel Weld, and David Wilkins. 1998. PDDL—The planning domain definition language. AIPS-98 Planning Committee (1998).Malik Ghallab, Dana Nau, and Paolo Traverso. 2004. Automated Planning. Theory and Practice. Morgan Kaufmann.Barbara J. Grosz, Luke Hunsberger, and Sarit Kraus. 1999. Planning and acting together. AI Mag. 20, 4 (1999), 23--34.Malte Helmert. 2004. A planning heuristic based on causal graph analysis. Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS’04), 161--170.Malte Helmert. 2006. The fast downward planning system. J. Artific. Intell. Res. 26, 1 (2006), 191--246.Malte Helmert and Carmel Domshlak. 2009. Landmarks, critical paths and abstractions: What’s the difference anyway? In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS’09). 162--169.Malte Helmert, Patrik Haslum, and Jörg Hoffmann. 2007. Flexible abstraction heuristics for optimal sequential planning. In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS’07). 176--183.Jörg Hoffmann and Bernhard Nebel. 2001. The FF planning system: Fast planning generation through heuristic search. J. Artific. Intell. Res. 14 (2001), 253--302.Jörg Hoffmann, Julie Porteous, and Laura Sebastiá. 2004. Ordered landmarks in planning. J. Artific. Intell. Res. 22 (2004), 215--278.Jan Hrncír, Michael Rovatsos, and Michal Jakob. 2015. Ridesharing on timetabled transport services: A multiagent planning approach. J. Intell. Transportat. Syst. 19, 1 (2015), 89--105.Loïg Jezequel and Eric Fabre. 2012. A#: A distributed version of A* for factored planning. In Proceedings of the 51th IEEE Conference on Decision and Control (CDC’12). 7377--7382.Anders Jonsson and Michael Rovatsos. 2011. Scaling up multiagent planning: A best-response approach. In Proceedings of the 21st International Conference on Automated Planning and Scheduling (ICAPS’11). AAAI, 114--121.Jaume Jordán and Eva Onaindia. 2015. Game-theoretic approach for non-cooperative planning. In Proceedings of the 29th Conference on Artificial Intelligence (AAAI’15). 1357--1363.Froduald Kabanza, Lu Shuyun, and Scott Goodwin. 2004. Distributed hierarchical task planning on a network of clusters. In Proceedings of the 16th International Conference on Parallel and Distributed Computing and Systems (PDCS’04). 139--140.Henry A. Kautz. 2006. Deconstructing planning as satisfiability. In Proceedings of the National Conference on Artificial Intelligence, Vol. 21, 1524.Elena Kelareva, Olivier Buffet, Jinbo Huang, and Sylvie Thiébaux. 2007. Factored planning using decomposition trees. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI’07). 1942--1947.Antonín Komenda, Michal Stolba, and Daniel L. Kovacs. 2016. The international competition of distributed and multiagent planners (CoDMAP). AI Mag. 37, 3 (2016), 109--115.Daniel L. Kovacs. 2012. A multi-agent extension of PDDL3.1. In Proceedings of the 3rd Workshop on the International Planning Competition (IPC’12). 19--27.Jonas Kvarnström. 2011. Planning for loosely coupled agents using partial order forward-chaining. In Proceedings of the 21st International Conference on Automated Planning and Scheduling (ICAPS’11). AAAI, 138--145.Victor Lesser, Keith Decker, Thomas Wagner, Norman Carver, Alan Garvey, Bryan Horling, Daniel Neiman, Rodion Podorozhny, M. Nagendra Prasad, Anita Raja, Regis Vincent, Ping Xuan, and X. Q. Zhang. 2004. Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton. Agents Multi-Agent Syst. 9, 1–2 (2004), 87--143.Derek Long, Henry Kautz, Bart Selman, Blai Bonet, Hector Geffner, Jana Koehler, Michael Brenner, Joerg Hoffmann, Frank Rittinger, Corin R. Anderson, Daniel S. Weld, David E. Smith, Maria Fox, and Derek Long. 2000. The AIPS-98 planning competition. AI Mag. 21, 2 (2000), 13--33.Nerea Luis and Daniel Borrajo. 2014. Plan merging by reuse for multi-agent planning. In Proceedings of the 2nd ICAPS Workshop on Distributed and Multi-Agent Planning (DMAP’14). 38--44.Nerea Luis and Daniel Borrajo. 2015. PMR: Plan merging by reuse. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 11--13.Shlomi Maliah, Ronen I. Brafman, and Guy Shani. 2017. Increased privacy with reduced communication in multi-agent planning. In Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS’17). 209--217.Shlomi Maliah, Guy Shani, and Roni Stern. 2014. Privacy preserving landmark detection. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). 597--602.Shlomi Maliah, Guy Shani, and Roni Stern. 2016. Collaborative privacy preserving multi-agent planning. Auton. Agents Multi-Agent Syst. (2016), 1--38.Felipe Meneguzzi and Lavindra de Silva. 2015. Planning in BDI agents: A survey of the integration of planning algorithms and agent reasoning. Knowl. Eng. Rev. 30, 1 (2015), 1--44.Christian Muise, Nir Lipovetzky, and Miquel Ramirez. 2015. MAP-LAPKT: Omnipotent multi-agent planning via compilation to classical planning. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 14--16.Dana S. Nau, Tsz-Chiu Au, Okhtay Ilghami, Ugur Kuter, J. William Murdock, Dan Wu, and Fusun Yaman. 2003. SHOP2: An HTN planning system. J. Artific. Intell. Res. 20 (2003), 379--404.Raz Nissim and Ronen I. Brafman. 2012. Multi-agent A* for parallel and distributed systems. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’12). 1265--1266.Raz Nissim and Ronen I. Brafman. 2013. Cost-optimal planning by self-interested agents. In Proceedings of the 27th Conference on Artificial Intelligence (AAAI’13).Raz Nissim and Ronen I. Brafman. 2014. Distributed heuristic forward search for multi-agent planning. J. Artific. Intell. Res. 51 (2014), 293--332.Raz Nissim, Ronen I. Brafman, and Carmel Domshlak. 2010. A general, fully distributed multi-agent planning algorithm. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’10). 1323--1330.Sergio Pajares and Eva Onaindia. 2013. Context-aware multi-agent planning in intelligent environments. Info. Sciences 227 (2013), 22--42.Michal Pechoucek, Martin Rehák, Petr Charvát, Tomáš Vlcek, and Michal Kolar. 2007. Agent-based approach to mass-oriented production planning: Case study. IEEE Trans. Syst. Man Cybernet. Part C 37, 3 (2007), 386--395.Damien Pellier. 2010. Distributed planning through graph merging. In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence (ICAART’10). 128--134.Miquel Ramirez, Nir Lipovetzky, and Christian Muise. 2015. Lightweight Automated Planning ToolKiT. Retrieved from http://lapkt.org/.Prashant P. Reddy and Manuela M. Veloso. 2011. Strategy learning for autonomous agents in smart grid markets. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11). 1446--1451.Silvia Richter and Matthias Westphal. 2010. The LAMA planner: Guiding cost-based anytime planning with landmarks. J. Artific. Intell. Res. 39, 1 (2010), 127--177.Valentin Robu, Han Noot, Han La Poutré, and Willem-Jan van Schijndel. 2011. A multi-agent platform for auction-based allocation of loads in transportation logistics. Expert Syst. Appl. 38, 4 (2011), 3483--3491.Óscar Sapena, Eva Onaindia, Antonio Garrido, and Marlene Arangú. 2008. A distributed CSP approach for collaborative planning systems. Eng. Appl. Artific. Intell. 21, 5 (2008), 698--709.Emilio Serrano, Jose M. Such, Juan A. Botía, and Ana García-Fornes. 2013. Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl. Intell. (2013), 1--16.Sven Seuken and Shlomo Zilberstein. 2008. Formal models and algorithms for decentralized decision making under uncertainty. Auton. Agents Multi-Agent Syst. 17, 2 (2008), 190--250.Guy Shani, Shlomi Maliah, and Roni Stern. 2016. Stronger privacy preserving projections for multi-agent planning. In Proceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS’16). 221--229.Claude E. Shannon. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 3 (1948), 379--423.Evren Sirin, Bijan Parsia, Dan Wu, James Hendler, and Dana Nau. 2004. HTN planning for web service composition using SHOP2. J. Web Semant. 1, 4 (2004), 377--396.Sarath Sreedharan, Yu Zhang, and Subbarao Kambhampati. 2015. A first multi-agent planner for required cooperation (MARC). In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 17--20.Michal Štolba, Daniel Fišer, and Antonín Komenda. 2015. Admissible landmark heuristic for multi-agent planning. In Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS’15). 211--219.Michal Štolba and Antonín Komenda. 2014. Relaxation heuristics for multiagent planning. In Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS’14). 298--306.Michal Štolba and Antonín Komenda. 2015. MADLA: Planning with distributed and local search. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 21--24.Michal Štolba, Jan Tožička, and Antonín Komenda. 2016. Quantifying privacy leakage in multi-agent planning. Proceedings of the 4rd ICAPS Workshop on Distributed and Multi-Agent Planning (DMAP’16). 80--88.Jose M. Such, Ana García-Fornes, Agustín Espinosa, and Joan Bellver. 2012. Magentix2: A privacy-enhancing agent platform. Eng. Appl. Artific. Intell. (2012), 96--109.Milind Tambe. 1997. Towards flexible teamwork. J. Artific. Intell. Res. 7 (1997), 83--124.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2012. An approach to multi-agent planning with incomplete information. In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI’12), Vol. 242. IOS Press, 762--767.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2014. A flexible coupling approach to multi-agent planning under incomplete information. Knowl. Info. Syst. 38, 1 (2014), 141--178.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2014. FMAP: Distributed cooperative multi-agent planning. Appl. Intell. 41, 2 (2014), 606--626.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2015. Global heuristics for distributed cooperative multi-agent planning. In Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS’15). 225--233.Alejandro Torreño, Óscar Sapena, and Eva Onaindia. 2015. MH-FMAP: Alternating global heuristics in multi-agent planning. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 25--28.Jan Tožička, Jan Jakubuv, and Antonín Komenda. 2015. PSM-based planners description for CoDMAP 2015 competition. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 29--32.Jan Tožička, Jan Jakubuv, Antonín Komenda, and Michal Pěchouček. 2015. Privacy-concerned multiagent planning. Knowl. Info. Syst. 48, 3 (2016), 581–618.Jan Tožička, Michal Štolba, and Antonín Komenda. 2017. The limits of strong privacy preserving multi-agent planning. In Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS’17). 221--229.Roman van der Krogt. 2007. Privacy loss in classical multiagent planning. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’07). 168--174.Roman van der Krogt. 2009. Quantifying privacy in multiagent planning. Multiagent Grid Syst. 5, 4 (2009), 451--469.David E. Wilkins. 1988. Practical Planning: Extending the Classical AI Planning Paradigm. Morgan Kaufmann.David E. Wilkins and Karen L. Myers. 1998. A multiagent planning architecture. In Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems (AIPS’98). 154--162.Michael Wolverton and Marie desJardins. 1998. Controlling communication in distributed planning using irrelevance reasoning. In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI’98). 868--874.Michael Wooldridge. 1997. Agent-based software engineering. IEEE Proc. Softw. Eng. 144, 1 (1997), 26--37.Yu Zhang and Subbarao Kambhampati. 2014. A formal analysis of required cooperation in multi-agent planning. CoRR abs/1404.5643 (2014). http://arxiv.org/abs/1404.5643

Separating actin-dependent chemokine receptor nanoclustering from dimerization indicates a role for clustering in CXCR4 signaling and function.

A current challenge in cell motility studies is to understand the molecular and physical mechanisms that govern chemokine receptor nanoscale organization at the cell membrane, and their influence on cell response. Using single-particle tracking and super-resolution microscopy, we found that the chemokine receptor CXCR4 forms basal nanoclusters in resting T cells, whose extent, dynamics, and signaling strength are modulated by the orchestrated action of the actin cytoskeleton, the co-receptor CD4, and its ligand CXCL12. We identified three CXCR4 structural residues that are crucial for nanoclustering and generated an oligomerization-defective mutant that dimerized but did not form nanoclusters in response to CXCL12, which severely impaired signaling. Overall, our data provide new insights to the field of chemokine biology by showing that receptor dimerization in the absence of nanoclustering is unable to fully support CXCL12-mediated responses, including signaling and cell function in vivoThis work was supported by grants from the Spanish Ministry of Economy and Competitiveness (SAF 2014-53416-R, SAF 2017-82940-R AEI/FEDER, EU) and the RETICS Program (RD16/0012/0006; RIER), Ministry of Economy and Competitiveness, Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2013-0347; SEV-2015-0522), and Fundación Privada Cellex and Generalitat de Catalunya (CERCA program). L.M.-M. is supported by the COMFUTURO program of the Spanish Research Council General Foundation.Peer reviewe