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The Impact of Durative State on Action Selection∗

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The Impact of Durative State on Action Selection∗ The Impact of Durative State on Action Selection∗ Joanna J. Bryson Konrad Lorenz Institute for Evolution and Cognition Research, A-3422 Altenberg, Austria Artificial models of natural Intelligence, University of Bath, Bath BA2 7AY, United Kingdom Abstract • structured dynamic plans, which arbitrate between currently-appropriate actions, providing real-time action Chemical / hormonal diffusion is the phylogenetically old- selection where there is immediate contention for an est form of biological action selection. In this paper we ar- agent’s resources (e.g. its physical location). gue its persistence in higher animals is a consequence of its utility in solving problems of dithering between high-level While one could theoretically imagine an intelligent agent goals. Chemical state underlying emotions and drives pro- being controlled entirely by a single system, e.g. an adap- vides greater persistence more easily than the electrical ac- tive neural network or an elaborate dynamic plan, we have tion potential systems underlying the fine details of action both demonstrated empirically (Bryson 2000b; 2001; Part- sequencing, while also providing periodicity and transience ington & Bryson 2005; Brom et al. 2006; Bryson & Leong not easily afforded in longer-term learning systems such as 2006) and argued from the history of AI agent construction synaptic plasticity. We argue that artificial real-time au- (Bryson 2000a) that the above sort of decomposition makes tonomous systems require similar systems, and review our the successful construction of an agent far more probable, own efforts and approaches to providing these. and makes its code easier to maintain. We now believe that this decomposition, while well- Introduction supported in the AI literature, is not entirely adequate. In recent years we have engaged in scientific modelling of non- Action selection is one of the fundamental problems of in- human primates using artificial life (Bryson & Leong 2006; telligence (Prescott, Bryson, & Seth 2007). For an agent Bryson, Ando, & Lehmann 2007), and creating VR hu- (whether biological or artificial), action selection is the on- manoid avatars (Tanguy, Willis, & Bryson 2006; 2007) going problem of choosing what to do next. For a devel- and intelligent game characters (Partington & Bryson 2005; oper, action selection presents two problems — designing Brom et al. 2006). This has lead us to improving our cod- the agent’s action-selection process, and determining the ing / description of long-term complete animal behaviour. level of abstraction at which the the process will operate. This improvement has required developing representations A physical agent must ultimately perform precise control of underlying emotional and other goal-directed behaviour ex- motors or muscles, but this is almost certainly not the level pression. at which decision making should occur. After a brief review, we describe an established AI ontol- In previous work we have described Behavior Oriented ogy. We then examine problems encountered, and describe Design (BOD) (Bryson & Thorisson´ 2000; Bryson & Stein expanding the system to solve them. These problems and 2001b; Bryson, Caulfield, & Drugowitsch 2005), a method- solutions may be salient for explaining the role of emotional ology for determining through iterative development the ap- expression in natural action selection, a question we visit propriate decomposition of intelligent behaviour into three throughout. categories: Durative State in Action-Selection Architectures • modules providing set primitive behaviours, which are coded using standard object-oriented languages, We believe durative state (in contrast to permanent or tran- sient) has not been adequately incorporated in standard • adaptive components of these modules, which are the action-selection architectures. By this we mean primarily mechanism through which the artificial agents can learn, emotions and drives, but we will also touch on conscious- and ness towards the end of the paper. ∗ Certainly many (if not most) current AI architectures do Research funded by the British Engineering and Physical Sci- address emotions and/or drives in some way. However, we ences Research Council (EPSRC) under the Adaptive and Interac- tive Behaviour of Animal & Computational Systems programme, do not think that the best-practice techniques of action se- grant number GR/S79299/01. lection have been fully integrated with an emotional sys- Copyright °c 2008, Association for the Advancement of Artificial tems. Some systems use emotions as relatively isolated sys- Intelligence (www.aaai.org). All rights reserved. tems, essentially social effectors for human-robot interac- tion (Breazeal & Scassellati 1999; De Rosis et al. 2003; To summarise our argument in advance, it is this. The Velasquez´ & Maes 1997). Outstanding in this group is Mar- components that ordinarily account for learning and execut- cella & Gratch 2002, whose emotional system also affects ing primitive actions are equivalent to synaptic-level con- action selection by changing their military training system’s nectivity and adaptation in the brain. These change at a basic reasoning in order to better simulate civilian reactions relatively slow pace, either through evolution (learning by to troops. Most AI research postulating agent-centric utility the species) or through development and lifelong-learning for affect have focussed on it as a sort of reward accumulator. by the agent. The components that account for the context- Emotions serve as additional pieces of internal state to as- appropriate expression of actions — that determine action sist in learning and applying action selection policies (Hiller ordering — are analogous to the electrical action potential 1995; Gadanho 1999; Zadeh, Shouraki, & Halavati 2006; in the brain. That is, dynamic planning systems correlate Broekens, Kosters, & Verbeek 2007). Some of this research to what cells are “firing” at any instant. This accounts for has been inspired partly by the controversial somatic marker changes of behaviour on the order of tens of milliseconds. hypothesis (Tomb et al. 2002)Several elaborate architec- These two types of components are the focus of this section. tures have been proposed but not yet constructed which pos- Action selection in nature also depends on a chemical / tulate similar referential or marker roles for emotional sys- hormonal systems which provides a third temporal duration tems, but operating at a higher self-referential level (Slo- for intelligent control. Like the electrical system, the chem- man & Croucher 1981; Norman, Ortony, & Russell 2003; ical control is temporary, (although both chemical and elec- Minsky, Singh, & Sloman 2004). These latter theories are trical selection can have lasting secondary impact by influ- beyond the scope of this paper, which deals with basic ac- encing long-term learning.) However, the chemical systems tion selection, not reflective reasoning. have durations measured in minutes or longer — across en- We believe emotions and drives are absolutely integral to tire episodes of behaviour. During this period they influence action selection, determining the current focus of behaviour metabolic shifts, thus affecting what actions are available to attention. The nearest correlates to our ideas include the an agent and how they are expressed, and provide additional work of Morgado & Gaspar (2005), but these mechanisms context for the instantaneous / electrical action selection sys- are not incorporated into a full architecture. At the other ex- tem, influencing its ‘decisions’. AI equivalents of this sort of treme, Breazeal (2003) also treats both moods and drives as control will be described in the section after this one, titled mechanisms for maintaining homeostatic goals, and has an Durative State. extremely elaborate robotic system built around them. The system we present here emphasises generalisation and us- Action ability, and is cast within a well-established AI architectural Every AI (and natural intelligence, NI) system must start framework: the hybrid or multi-layer architecture, which from some set of primitive affordances — control the agent’s features both modules for programmability and hierarchi- intelligence can access. At the most basic level this may be cal plan structures for coding agent priorities or personality. seen as degrees of freedom for the agent.For both NI and AI, Below we describe emotions and drives incorporated into a generally ’intelligent’ control is exercised at a more abstract general-purpose, publicly-available open-source modular AI level. In NI, what intelligence controls co-evolves and co- control system. develops along with how it is controlled. Skill compilation is We originally believed durative state should be repre- also a popular idea in AI (see e.g. Newell 1990) although to sented simply in behaviour modules like other variable state. date it is not often used in commercial / production-quality We were wrong. We were wrong. We now believe that a sys- systems due to issues of combinatorics and the fact that sys- tem more like spreading-activation networks is needed for tems that learn in unregulated ways can often break them- influencing some of the selection of the highest-level goals. selves (Bryson & Hauser 2002)1. Of course, spreading activation has been shown intractable One widely-utilised methodology for creating action for the problem of action selection as a whole due to poor primitives in real-time, production-quality systems is to use scaling (Tyrrell
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