Projective simulation with generalization

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent's performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.Comment: 14 pages, 9 figure

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

A Unified Theory of Dual-Process Control

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles

Luovat järjestelmät, toimijat sekä yhteisöt : Teoreettisia analyysimenetelmiä ja empiirisiä yhteistyötutkimuksia

Creativity is a multi-faceted phenomenon that can be observed in diverse individuals and contexts, both natural and artificial. This thesis studies computational creativity, i.e. creativity in machines, which can be broadly categorised as a subfield of artificial intelligence. In particular, the thesis deals with three important perspectives on computational creativity: (1) identifying properties of creative individuals, (2) studying processes that lead to creative outcomes, and (3) observing and analysing social aspects of creativity, e.g. collaboration which may allow the individuals to create something together which they could not do alone. One of the key interests in computational creativity is how computational entities may exhibit creativity in their own right, implying that the creative entities and their compositions, roles, processes and interactions are potentially different from those encountered in nature. This calls for theoretical analysis methods specifically tailored for artificial creative entities, and carefully controlled empirical experiments and simulations with them. We study both of these aspects. The analysis methods allow us to scrutinise exactly how creativity occurs in artificial entities by providing appropriate conceptual elements and vocabulary, while experiments enable us to test and confirm the effectiveness of different design decisions considering individual artificial creative entities and their interaction with each other. We propose three novel, domain-general analysis tools for artificial creative entities, i.e. creative systems and creative agents, and collections of them, called creative societies. First, we distinguish several conceptual components relevant for metacreative systems, i.e. systems that can reflect and control their creative behaviour, and discuss how these components are interlinked and affect the system's creativity. Second, we merge elements from sequential decision making in intelligent agents, i.e. Markov Decision Processes, into formal creativity as search model called the Creative Systems Framework, providing a detailed account of various elements which compose the decision-making process of a creative agent. Third, we map elements from an eminent social creativity theory, the Systems View of Creativity, a.k.a. Domain-Individual-Field-Interaction model, into the elements of the Creative Systems Framework and show how creative societies may be analysed formally with it. Each of the proposed analysis tools provides new ways to analyse creativity in artificial entities. The analysis of metacreative systems assumes an architectural point of view to creativity, which has not been previously addressed in detail. Deconstructing the decision-making process of a creative agent gives us additional means to discuss and understand why or how a creative agent selects certain actions. Lastly, the contributions to the creative societies are the first formal framework for their analysis. We also investigate in two consecutive case studies collaborator selection in creative societies. In the first study, we focus on what kind of cues, e.g. selfish or altruistic, assist in choosing beneficial collaboration partners when all the agents can observe from their peers are the individually created end products. The second study allows the agents to adjust their aesthetic preferences during the simulations and inspects what emerges from society as a whole. We conclude that selfish cues seem to be more effective in choosing the collaboration partners in our settings and that the society exhibits distinct emergence depending on how much the agents are willing to change their aesthetic preferences.Luovuus on monitahoinen ilmiö, jonka osatekijöitä voidaan tunnistaa monissa eri asiayhteyksissä. Tässä väitöskirjassa käsitellään laskennallista luovuutta, eli luovuutta koneissa, mikä voidaan karkeasti luokitella tekoälyn yhdeksi osa-alueeksi. Yksi laskennallisen luovuuden tärkeimmistä kiinnostuksen kohteista tutkii miten koneet voivat olla luovia omasta ansiostaan. Tämä tarkoittaa että luovat järjestelmät ja toimijat, menetelmät, yhteisöt sekä niiden vuorovaikutus voivat erota ihmisten vastaavista. On siis tärkeää kyetä keskustelemaan luovien järjestelmien, toimijoiden ja yhteisöjen ominaisuuksista riippumatta niiden toteutuksien yksityiskohdista sekä suunnittelemaan simulaatioita ja kokeita joissa voidaan todentaa suunnitteluratkaisujen vaikutukset järjestelmän luovuudelle. Väitöskirja esittelee kolme uutta luovuuden analyysimenetelmää, jotka on kehitetty analysoimaan (1) luovia järjestelmiä, (2) luovia toimijoita sekä (3) luovia agenttiyhteisöjä. Lisäksi kahdessa osajulkaisussa tutkitaan yhteistyöprosesseja simuloiduissa yhteisöissä, joissa itsenäiset luovat toimijat tuottavat abstraktia taidetta evolutiivisia menetelmiä käyttäen. Ehdotetut analyysimenetelmät mahdollistavat luovuuden monialaisen tarkastelun sekä tarjoavat yhden mahdollisen suunnan kohti laskennallisen luovuuden yhtenäistä analyysimenetelmää. Havainnot empiirisistä simulaatioista antavat uutta tietoa laskennallisista yhteistyöprosesseista ja ovat askel kohti monimutkaisempia kokeita luovan yhteistyön saralla

Software tools for the cognitive development of autonomous robots

Robotic systems are evolving towards higher degrees of autonomy. This paper reviews the cognitive tools available nowadays for the fulfilment of abstract or long-term goals as well as for learning and modifying their behaviour.Peer ReviewedPostprint (author's final draft

Egocentric Planning for Scalable Embodied Task Achievement

Embodied agents face significant challenges when tasked with performing actions in diverse environments, particularly in generalizing across object types and executing suitable actions to accomplish tasks. Furthermore, agents should exhibit robustness, minimizing the execution of illegal actions. In this work, we present Egocentric Planning, an innovative approach that combines symbolic planning and Object-oriented POMDPs to solve tasks in complex environments, harnessing existing models for visual perception and natural language processing. We evaluated our approach in ALFRED, a simulated environment designed for domestic tasks, and demonstrated its high scalability, achieving an impressive 36.07% unseen success rate in the ALFRED benchmark and winning the ALFRED challenge at CVPR Embodied AI workshop. Our method requires reliable perception and the specification or learning of a symbolic description of the preconditions and effects of the agent's actions, as well as what object types reveal information about others. It is capable of naturally scaling to solve new tasks beyond ALFRED, as long as they can be solved using the available skills. This work offers a solid baseline for studying end-to-end and hybrid methods that aim to generalize to new tasks, including recent approaches relying on LLMs, but often struggle to scale to long sequences of actions or produce robust plans for novel tasks