Effects of Multiple Learning Mechanisms in a Cognitive Architecture

Effects of Multiple Learning Mechanisms in a Cognitive Architecture Dongkyu Choi (dongkyuc@uic.edu) Stellan Ohlsson (stellan@uic.edu) Department of Psychology University of Illinois at Chicago 1007 W Harrison Street (M/C 285), Chicago, IL 60607 USA Abstract I CARUS makes distinctions in two separate dimensions. The first exists between concepts and skills. Concepts give I CARUS a language to describe its surroundings by enabling the system to infer beliefs about the current state of the world. Skills, on the other hand, are procedures that I CARUS believes to achieve certain concept instances. The second distinction lies between long-term knowledge and short-term structures. Long-term concepts and skills are general descriptions of sit- uations and procedures, and I CARUS instantiates them for a particular situation at hand. Instantiated concepts and skills are short-term structures, in that they are constantly created and destroyed as the situation changes. These two distinc- tions result in four separate memories in I CARUS . Human learning involves multiple sources of information. Their ability to adapt to changes in the environment depends on having such multiple learning modes. In this paper, we ex- tend an existing cognitive architecture to have three distinct learning modes, in an effort to test the hypothesis that multiple learning capabilities bring synergistic effect in the overall per- formance. We show experimental results in a simplified route generation domain. Keywords: cognitive architecture, learning, multiple modes of learning, learning from success, learning from failures, learning declarative knowledge, skill acquisition Introduction People acquire knowledge from various sources. They learn from their own success and failures, by observing situations around them, and by imitating others’ behavior. Their abil- ity to adapt to changing situations depends on such multiple modes of learning (Ohlsson, 2011). There have been many previous work on each of such learning modes, but we are particularly interested in their interactions and the synergy among them. Since cognitive architectures (Newell, 1990) provide gen- eral framework for modeling cognition, they are suitable for our research on multiple learning modes and their in- teractions. In this work, we use one such architecture, I CARUS , that supports both learning from success (Langley & Choi, 2006) and learning from failures (Choi & Ohlsson, 2010; Ohlsson, 1996). We extended the architecture with a third mode of learning declarative knowledge in an effort to broaden the scope of our research, although we do not yet perform a lesion study in the current work with this learning mode turned off. In the following sections, we first review some basic as- sumptions of I CARUS starting with its representation and memories and continuing to the inference and execution mechanisms. We then describe the three learning mecha- nisms in some detail and show experimental results that show synergy in a route generation domain. We also discuss related and future work before we conclude. Representation and Memories As with other cognitive architectures (Laird et al., 1986; Anderson, 1993), I CARUS makes commitments to a specific way to represent knowledge, infer beliefs, perform execu- tion and learn new knowledge. In this section, we review I CARUS ’s representation of knowledge and the correspond- ing memories. In its long-term conceptual memory, the architecture en- codes concept definitions that are similar to Horn clauses (Horn, 1951). As shown in Table 1, concepts include a head and a body that includes perceptual matching conditions or references to other concepts. The first concept with the head, (at ?location), matches against an object of type, self, and its attribute, location, in its :percepts field. The second concept, (connected ?from ?to), matches against an additional object of type, location, and tests if its accessible attribute is not null and the two locations, ?from and ?to, are different. These two concepts do not have any reference to other concepts in their definitions, so they are primitive concepts. On the other hand, the third concept, (not-dead-end ?location), matches against a location object and refers to two subconcepts in addition to a test. This makes the last concept a non-primitive one. This way, I CARUS builds a hierarchy of concepts that provides multiple levels of abstraction. Another long-term memory stores I CARUS ’s skills that re- semble S TRIPS operators (Fikes & Nilsson, 1971). The head of each skill is the predicate it is known to achieve, making all skills indexed by their respective goals. Each skill has a body that includes perceptual matching conditions, some preconditions, and either direct actions to the world or a sub- goal decomposition. Skills with no references to subgoals are primitive, while the ones with subgoals are non-primitive. Table 2 shows some sample I CARUS skills. The first skill that achieves (at ?location) has two preconditions, (at ?from) and (connected ?from ?location), in its :start field and an action in its :actions field. Without any ref- erence to subgoals, this skill is primitive. The second skill, however, is a non-primitive one that provides a subgoal de- composition to achieve (at B). Namely, this skill instructs