LIP6

In contemporary art, a new type of artworks use motion as a material from which to create the illusion of life. These autonomous robotic artworks have a behavioral specificity; they tend to be perceived as living, and by some account intentional entities. To account for this behavioral specifity and how it affects the audience experience, we propose a data-driven approach to reveal specific visit patterns. Through a cluster analysis performed on visitors’ path inside an installation involving autonomous objects, we highlight four different attitudes characterized by patterns of approach or withdrawal, passive observation and exploration.

In contemporary art, a new type of artworks use motion as a material from which to create the illusion of life. These autonomous robotic artworks have a behavioral specificity; they tend to be perceived as living, and by some account intentional entities. To account for this behavioral specifity and how it affects the audience experience, we propose a data-driven approach to reveal specific visit patterns. Through a cluster analysis performed on visitors' path inside an installation involving autonomous objects, we highlight four different attitudes characterized by patterns of approach or withdrawal, passive observation and exploration.

This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance of the BDI model of rational agency, little work has been done to deal with its two main limitations: i) The lack of learning competences; and ii) The lack of explicit multiagent functionality. From the multi-agent learning perspective, we propose a BDI agent architecture extended with learning competences for MAS context. Induction of Logical Decision Trees, a first order method, is used to enable agents to learn when their plans are successfully executable. Our implementation enables multiple agents executed as parallel functions in a single Lisp image. In addition, our approach maintains consistency between learning and the theory of practical reasoning. a learning agent by Stuart Russell and Peter Norvig . Design choices were constrained by the fact that BDI agents perform practical reasoning to behave. Practical reasoning, together with BDI semantics, pose a hard design problem: Learning methods directed towards action, very popular in MAS learning, use representations less expressive than those used in the BDI model, i.e., basically propositional representations as in classic reinforcement learning ; Learning methods with more expressive representations are usually conceived as isolated learning systems, directed towards epistemic reasoning. This may explain why in the abundant MAS learning literature, only Olivia et al. has considered the problem of BDI learning agents, despite the relevance of the model in agency and MAS. The approach we use to solve this problem, applies Inductive Logic Programming (ILP) [20] methods, particularly Induction of Logical Decision Trees [4], to learn when plans are executable, as expressed by the context of plans, i.e., the components of the BDI model behind choices in practical reasoning.