Semiotic Dynamics Solves the Symbol Grounding Problem

Language requires the capacity to link symbols (words, sentences) through the intermediary of internal representations to the physical world, a process known as symbol grounding. One of the biggest debates in the cognitive sciences concerns the question how human brains are able to do this. Do we need a material explanation or a system explanation? John Searle's well known Chinese Room thought experiment, which continues to generate a vast polemic literature of arguments and counter-arguments, has argued that autonomously establishing internal representations of the world (called 'intentionality' in philosophical parlance) is based on special properties of human neural tissue and that consequently an artificial system, such as an autonomous physical robot, can never achieve this. Here we study the Grounded Naming Game as a particular example of symbolic interaction and investigate a dynamical system that autonomously builds up and uses the semiotic networks necessary for performance in the game. We demonstrate in real experiments with physical robots that such a dynamical system indeed leads to a successful emergent communication system and hence that symbol grounding and intentionality can be explained in terms of a particular kind of system dynamics. The human brain has obviously the right mechanisms to participate in this kind of dynamics but the same dynamics can also be embodied in other types of physical systems

Qualitative Analysis of POMDPs with Temporal Logic Specifications for Robotics Applications

We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-time temporal logic (LTL) can be expressed as parity objectives. We study the qualitative analysis problem for POMDPs with parity objectives that asks whether there is a controller (policy) to ensure that the objective holds with probability 1 (almost-surely). While the qualitative analysis of POMDPs with parity objectives is undecidable, recent results show that when restricted to finite-memory policies the problem is EXPTIME-complete. While the problem is intractable in theory, we present a practical approach to solve the qualitative analysis problem. We designed several heuristics to deal with the exponential complexity, and have used our implementation on a number of well-known POMDP examples for robotics applications. Our results provide the first practical approach to solve the qualitative analysis of robot motion planning with LTL properties in the presence of uncertainty

Measurement and display of control information. Remote manipulation and manual control Progress report, 1 Apr. - 30 Sep. 1967

Control interface between man and computer manipulators, and optimality of human controllers as time optimal, bang-bang state regulators of second order system

Economic Games as Estimators

Discrete event games are discrete time dynamical systems whose state transitions are discrete events caused by actions taken by agents within the game. The agents’ objectives and associated decision rules need not be known to the game designer in order to impose struc- ture on a game’s reachable states. Mechanism design for discrete event games is accomplished by declaring desirable invariant properties and restricting the state transition functions to conserve these properties at every point in time for all admissible actions and for all agents, using techniques familiar from state-feedback control theory. Building upon these connections to control theory, a framework is developed to equip these games with estimation properties of signals which are private to the agents playing the game. Token bonding curves are presented as discrete event games and numerical experiments are used to investigate their signal processing properties with a focus on input-output response dynamics.Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man

We present an application of Monte Carlo tree search (MCTS) for the game of Ms Pac-Man. Contrary to most applications of MCTS to date, Ms Pac-Man requires almost real-time decision making and does not have a natural end state. We approached the problem by performing Monte Carlo tree searches on a five player maxn tree representation of the game with limited tree search depth. We performed a number of experiments using both the MCTS game agents (for pacman and ghosts) and agents used in previous work (for ghosts). Performance-wise, our approach gets excellent scores, outperforming previous non-MCTS opponent approaches to the game by up to two orders of magnitude. © 2011 IEEE