452 research outputs found
Can agents without concepts think? an investigation using a knowledge based system
Grid-World is a working computer model which has been used to investigate the search capabilities of artificial agents that understand the world in terms of non-conceptual content. The results from this model show that the non-conceptual agent outperformed the stimulus response agent, and both were outperformed by the conceptual agent. This result provides quantitative evidence to support the theoretical argument that animals and pre-linguistic children may use non-conceptual content to understand the world. Modelling these ideas in an artificial environment provides an opportunity for a new approach to artificial intelligence
Spatial representation for planning and executing robot behaviors in complex environments
Robots are already improving our well-being and productivity in
different applications such as industry, health-care and indoor
service applications. However, we are still far from developing (and
releasing) a fully functional robotic agent that can autonomously
survive in tasks that require human-level
cognitive capabilities. Robotic systems on the market, in fact, are
designed to address specific applications, and can only run
pre-defined behaviors to robustly repeat few tasks (e.g., assembling
objects parts, vacuum cleaning). They internal representation of the
world is usually constrained to the task they are performing, and
does not allows for generalization to other
scenarios. Unfortunately, such a paradigm only apply to a very
limited set of domains, where the environment can be assumed to be
static, and its dynamics can be handled before
deployment. Additionally, robots configured in this way will
eventually fail if their "handcrafted'' representation of the
environment does not match the external world.
Hence, to enable more sophisticated cognitive skills, we investigate
how to design robots to properly represent the environment and
behave accordingly. To this end, we formalize a representation of
the environment that enhances the robot spatial knowledge to
explicitly include a representation of its own actions. Spatial
knowledge constitutes the core of the robot understanding of the
environment, however it is not sufficient to represent what the
robot is capable to do in it. To overcome such a limitation, we
formalize SK4R, a spatial knowledge representation for robots which
enhances spatial knowledge with a novel and "functional"
point of view that explicitly models robot actions. To this end, we
exploit the concept of affordances, introduced to express
opportunities (actions) that objects offer to an agent. To encode
affordances within SK4R, we define the "affordance
semantics" of actions that is used to annotate an environment, and
to represent to which extent robot actions support goal-oriented
behaviors.
We demonstrate the benefits of a functional representation of the
environment in multiple robotic scenarios that traverse and
contribute different research topics relating to: robot knowledge
representations, social robotics, multi-robot systems and robot
learning and planning. We show how a domain-specific representation,
that explicitly encodes affordance semantics, provides the robot
with a more concrete understanding of the environment and of the
effects that its actions have on it. The goal of our work is to
design an agent that will no longer execute an action, because of
mere pre-defined routine, rather, it will execute an actions because
it "knows'' that the resulting state leads one step closer to
success in its task
Signifiers as a First-class Abstraction in Hypermedia Multi-Agent Systems
Hypermedia APIs enable the design of reusable hypermedia clients that
discover and exploit affordances on the Web. However, the reusability of such
clients remains limited since they cannot plan and reason about interaction.
This paper provides a conceptual bridge between hypermedia-driven affordance
exploitation on the Web and methods for representing and reasoning about
actions that have been extensively explored for Multi-Agent Systems (MAS) and,
more broadly, Artificial Intelligence. We build on concepts and methods from
Affordance Theory and Human-Computer Interaction that support interaction
efficiency in open and evolvable environments to introduce signifiers as a
first-class abstraction in Web-based MAS: Signifiers are designed with respect
to the agent-environment context of their usage and enable agents with
heterogeneous abilities to act and to reason about action. We define a formal
model for the contextual exposure of signifiers in hypermedia environments that
aims to drive affordance exploitation. We demonstrate our approach with a
prototypical Web-based MAS where two agents with different reasoning abilities
proactively discover how to interact with their environment by perceiving only
the signifiers that fit their abilities. We show that signifier exposure can be
inherently managed based on the dynamic agent-environment context towards
facilitating effective and efficient interactions on the Web
Life is an Adventure! An agent-based reconciliation of narrative and scientific worldviews\ud
The scientific worldview is based on laws, which are supposed to be certain, objective, and independent of time and context. The narrative worldview found in literature, myth and religion, is based on stories, which relate the events experienced by a subject in a particular context with an uncertain outcome. This paper argues that the concept of “agent”, supported by the theories of evolution, cybernetics and complex adaptive systems, allows us to reconcile scientific and narrative perspectives. An agent follows a course of action through its environment with the aim of maximizing its fitness. Navigation along that course combines the strategies of regulation, exploitation and exploration, but needs to cope with often-unforeseen diversions. These can be positive (affordances, opportunities), negative (disturbances, dangers) or neutral (surprises). The resulting sequence of encounters and actions can be conceptualized as an adventure. Thus, the agent appears to play the role of the hero in a tale of challenge and mystery that is very similar to the "monomyth", the basic storyline that underlies all myths and fairy tales according to Campbell [1949]. This narrative dynamics is driven forward in particular by the alternation between prospect (the ability to foresee diversions) and mystery (the possibility of achieving an as yet absent prospect), two aspects of the environment that are particularly attractive to agents. This dynamics generalizes the scientific notion of a deterministic trajectory by introducing a variable “horizon of knowability”: the agent is never fully certain of its further course, but can anticipate depending on its degree of prospect
Value Co-Creation in Smart Services: A Functional Affordances Perspective on Smart Personal Assistants
In the realm of smart services, smart personal assistants (SPAs) have become a popular medium for value co-creation between service providers and users. The market success of SPAs is largely based on their innovative material properties, such as natural language user interfaces, machine learning-powered request handling and service provision, and anthropomorphism. In different combinations, these properties offer users entirely new ways to intuitively and interactively achieve their goals and thus co-create value with service providers. But how does the nature of the SPA shape value co-creation processes? In this paper, we look through a functional affordances lens to theorize about the effects of different types of SPAs (i.e., with different combinations of material properties) on users’ value co-creation processes. Specifically, we collected SPAs from research and practice by reviewing scientific literature and web resources, developed a taxonomy of SPAs’ material properties, and performed a cluster analysis to group SPAs of a similar nature. We then derived 2 general and 11 cluster-specific propositions on how different material properties of SPAs can yield different affordances for value co-creation. With our work, we point out that smart services require researchers and practitioners to fundamentally rethink value co-creation as well as revise affordances theory to address the dynamic nature of smart technology as a service counterpart
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
To pass or not to pass:modeling the movement and affordance dynamics of a pick and place task
Humans commonly engage in tasks that require or are made more efficient by coordinating with other humans. In this paper we introduce a task dynamics approach for modeling multi-agent interaction and decision making in a pick and place task where an agent must move an object from one location to another and decide whether to act alone or with a partner. Our aims were to identify and model (1) the affordance related dynamics that define an actor's choice to move an object alone or to pass it to their co-actor and (2) the trajectory dynamics of an actor's hand movements when moving to grasp, relocate, or pass the object. Using a virtual reality pick and place task, we demonstrate that both the decision to pass or not pass an object and the movement trajectories of the participants can be characterized in terms of a behavioral dynamics model. Simulations suggest that the proposed behavioral dynamics model exhibits features observed in human participants including hysteresis in decision making, non-straight line trajectories, and non-constant velocity profiles. The proposed model highlights how the same low-dimensional behavioral dynamics can operate to constrain multiple (and often nested) levels of human activity and suggests that knowledge of what, when, where and how to move or act during pick and place behavior may be defined by these low dimensional task dynamics and, thus, can emerge spontaneously and in real-time with little a priori planning
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