Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Evaluating a reinforcement learning algorithm with a general intelligence test

In this paper we apply the recent notion of anytime universal intelligence tests to the evaluation of a popular reinforcement learning algorithm, Q-learning. We show that a general approach to intelligence evaluation of AI algorithms is feasible. This top-down (theory-derived) approach is based on a generation of environments under a Solomonoff universal distribution instead of using a pre-defined set of specific tasks, such as mazes, problem repositories, etc. This first application of a general intelligence test to a reinforcement learning algorithm brings us to the issue of task-specific vs. general AI agents. This, in turn, suggests new avenues for AI agent evaluation and AI competitions, and also conveys some further insights about the performance of specific algorithms. © 2011 Springer-Verlag.We are grateful for the funding from the Spanish MEC and MICINN for projects TIN2009-06078-E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062-C02, for MEC FPU grant AP2006-02323, and Generalitat Valenciana for Prometeo/2008/051.Insa Cabrera, J.; Dowe, DL.; Hernández Orallo, J. (2011). Evaluating a reinforcement learning algorithm with a general intelligence test. En Advances in Artificial Intelligence. Springer Verlag (Germany). 7023:1-11. https://doi.org/10.1007/978-3-642-25274-7_1S1117023Dowe, D.L., Hajek, A.R.: A non-behavioural, computational extension to the Turing Test. In: Intl. Conf. on Computational Intelligence & multimedia applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (1998)Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competition. AI Magazine 26(2), 62 (2005)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In: Hutter, M., et al. (eds.) 3rd Intl. Conf. on Artificial General Intelligence, Atlantis, pp. 182–183 (2010)Hernández-Orallo, J.: On evaluating agent performance in a fixed period of time. In: Hutter, M., et al. (eds.) 3rd Intl. Conf. on Artificial General Intelligence, pp. 25–30. Atlantis Press (2010)Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Legg, S., Hutter, M.: A universal measure of intelligence for artificial agents. Intl. Joint Conf. on Artificial Intelligence, IJCAI 19, 1509 (2005)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Levin, L.A.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)Li, M., Vitányi, P.: An introduction to Kolmogorov complexity and its applications, 3rd edn. Springer-Verlag New York, Inc. (2008)Sanghi, P., Dowe, D.L.: A computer program capable of passing IQ tests. In: Proc. 4th ICCS International Conference on Cognitive Science (ICCS 2003), Sydney, Australia, pp. 570–575 (2003)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and Control 7(1), 1–22 (1964)Strehl, A.L., Li, L., Wiewiora, E., Langford, J., Littman, M.L.: PAC model-free reinforcement learning. In: Proc. of the 23rd Intl. Conf. on Machine Learning, ICML 2006, New York, pp. 881–888 (2006)Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. The MIT press (1998)Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)Veness, J., Ng, K.S., Hutter, M., Silver, D.: Reinforcement learning via AIXI approximation. In: Proc. 24th Conf. on Artificial Intelligence (AAAI 2010), pp. 605–611 (2010)Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine learning 8(3), 279–292 (1992)Weyns, D., Parunak, H.V.D., Michel, F., Holvoet, T., Ferber, J.: Environments for multiagent systems state-of-the-art and research challenges. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 1–47. Springer, Heidelberg (2005)Whiteson, S., Tanner, B., White, A.: The Reinforcement Learning Competitions. The AI magazine 31(2), 81–94 (2010)Woergoetter, F., Porr, B.: Reinforcement learning. Scholarpedia 3(3), 1448 (2008)Zatuchna, Z., Bagnall, A.: Learning mazes with aliasing states: An LCS algorithm with associative perception. Adaptive Behavior 17(1), 28–57 (2009

On environment difficulty and discriminating power

The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-014-9257-1This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multiagent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multiagent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.I thank the reviewers for their comments, especially those aiming at a clearer connection with the field of multi-agent systems and the suggestion of better approximations for the calculation of the response curves. The implementation of the elementary cellular automata used in the environments is based on the library 'CellularAutomaton' by John Hughes for R [58]. I am grateful to Fernando Soler-Toscano for letting me know about their work [65] on the complexity of 2D objects generated by elementary cellular automata. I would also like to thank David L. Dowe for his comments on a previous version of this paper. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).José Hernández-Orallo (2015). On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems. 29(3):402-454. https://doi.org/10.1007/s10458-014-9257-1S402454293Anderson, J., Baltes, J., & Cheng, C. T. (2011). 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Universal psychometrics: measuring cognitive abilities in the machine kingdom

We present and develop the notion of ‘universal psychometrics’ as a subject of study, and eventually a discipline, that focusses on the measurement of cognitive abilities for the machine kingdom, which comprises any (cognitive) system, individual or collective, either artificial, biological or hybrid. Universal psychometrics can be built, of course, upon the experience, techniques and methodologies from (human) psychometrics, comparative cognition and related areas. Conversely, the perspective and techniques which are being developed in the area of machine intelligence measurement using (algorithmic) information theory can be of much broader applicability and implication outside artificial intelligence. This general approach to universal psychometrics spurs the re-understanding of most (if not all) of the big issues about the measurement of cognitive abilities, and creates a new foundation for (re)defining and mathematically formalising the concept of cognitive task, evaluable subject, interface, task choice, difficulty, agent response curves, etc. We introduce the notion of a universal cognitive test and discuss whether (and when) it may be necessary for exploring the machine kingdom. On the issue of intelligence and very general abilities, we also get some results and connections with the related notions of no-free-lunch theorems and universal priorsWe thank the anonymous reviewers for their comments. This work was supported by the MEC-MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST -European Cooperation in the field of Scientific and Technical Research IC0801 ATHernández Orallo, J.; Dowe, DL.; Hernández Lloreda, MV. (2014). Universal psychometrics: measuring cognitive abilities in the machine kingdom. Cognitive Systems Research. 27:50-74. https://doi.org/10.1016/j.cogsys.2013.06.001S50742

Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. 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AgentP : a learning classifier system with associative perception in maze environments

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AgentP model: Learning classifier system with associative perception

Abstract. Aliasing environments present the tasks of increased difficulty for Learning Classifier Systems. Aliasing squares look identical for an agent with limited perceptive power, but may demand a completely different optimal strategy. Thus, the presence of aliasing squares in a maze may lead to a non-optimal behaviour and decrease the agent’s performance. As a possible approach to the problem we introduce a psychological model of associative perception learning and based on the model AgentP, an LCS with explicitly imprinted images of the environmental states. The system is tested on a few aliasing and non-aliasing environments to establish the learning effectiveness of the approach

A Reinforcement Learning Agent with Associative Perception

One of the most perspective ideas of further development of Reinforcement Learning (RL) research involves using associative learning models to improve performance of reinforcement learning agents. Learning Classifier Systems (LCS) have proved to be one of the most successful classes of RL methods that have been applied to maze environments. However, so far LCS have shown their effectiveness for small sized and simple maze environment tasks only. We try to overcome the limits by tying up the connection between LCS performance and principles of established psychological phenomena, those of associative learning in particular. We bring together the ideas of imprinting, laws of organization and stimulus generalization to create a basis for introducing an associative perception and recognition to the LCS framework. As a result, we develop the Associative Perception Learning Model, a new concept for modelling the learning process in autonomous learning agents. The model has been implemented as AgentP, a new LCS with Associative Perception and its performance has been evaluated on existing and new maze problems

On the Classification of Maze Problems

A maze is a grid-like two-dimensional area of any size, usually rectangular. A maze consists of cells. A cell is an elementary maze item, a formally bounded space, interpreted as a single site. The maze may contain different obstacles in any quantity. Some may be significant for learning purposes, like virtual food. The agent is randomly placed in the maze on an empty cell. The agent is allowed to move in all directions, but only through empty space. The task is to learn a policy to reach food as fast as possible from any square. Once the food is reached, the agent position is reset to a random one and the task repeated