A computational framework for aesthetical navigation in musical search space

Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper

Why innovation theories make no sense

In this paper I argue that it makes no sense to have "innovation theories", or the use of the concept in describing the potential of social and economic theories to explain the phenomenon of non-equilibrium. If we wish to explain dynamic, change, evolution, revolution, etc. in socio-economic systems, then theories that are genuinely capable of doing so are indispensable. We don't need static theories of society, economy, organization, the firm, etc. which need an "additional" theory of incongruence and dynamics as an exception. In this context, the recent boom of literature on "social innovation" seems to be particularly questionable. It presents itself with the attitude of opening, broadening, or intellectually "freeing" the discourse of innovation from its technological insularity. That might be interpreted as opportunism since the idea of "innovation" generates an abundance of attention and approval so that any matter of concern can be legitimated and ennobled by the simple use of the word. Therefore, my paper begins with a short history of the category which has never been restricted to techno semantics. --

Personas versus clones for player decision modelling

The current paper investigates how to model human play styles. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Two methods are developed, applied, and compared: procedural personas, based on utilities designed with expert knowledge, and clones, trained to reproduce play traces. Additionally, two metrics for comparing agent and human decision making styles are proposed and compared. Results indicate that personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play traces.peer-reviewe

Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting---where the true reward function is unknown and only samples of expert behavior are given. We propose a sampling method based on Bayesian inverse reinforcement learning that uses demonstrations to determine practical high-confidence upper bounds on the $\alpha$-worst-case difference in expected return between any evaluation policy and the optimal policy under the expert's unknown reward function. We evaluate our proposed bound on both a standard grid navigation task and a simulated driving task and achieve tighter and more accurate bounds than a feature count-based baseline. We also give examples of how our proposed bound can be utilized to perform risk-aware policy selection and risk-aware policy improvement. Because our proposed bound requires several orders of magnitude fewer demonstrations than existing high-confidence bounds, it is the first practical method that allows agents that learn from demonstration to express confidence in the quality of their learned policy.Comment: In proceedings AAAI-1

Knowledge sharing as spontaneous order : on the emergence of strong and weak ties

Not availabl